no code implementations • NAACL (GeBNLP) 2022 • Xiuying Chen, Mingzhe Li, Rui Yan, Xin Gao, Xiangliang Zhang
Word embeddings learned from massive text collections have demonstrated significant levels of discriminative biases. However, debias on the Chinese language, one of the most spoken languages, has been less explored. Meanwhile, existing literature relies on manually created supplementary data, which is time- and energy-consuming. In this work, we propose the first Chinese Gender-neutral word Embedding model (CGE) based on Word2vec, which learns gender-neutral word embeddings without any labeled data. Concretely, CGE utilizes and emphasizes the rich feminine and masculine information contained in radicals, i. e., a kind of component in Chinese characters, during the training procedure. This consequently alleviates discriminative gender biases. Experimental results on public benchmark datasets show that our unsupervised method outperforms the state-of-the-art supervised debiased word embedding models without sacrificing the functionality of the embedding model.
no code implementations • EMNLP 2020 • Zhangming Chan, Yuchi Zhang, Xiuying Chen, Shen Gao, Zhiqiang Zhang, Dongyan Zhao, Rui Yan
(2) generate a post including selected products via the MGenNet (Multi-Generator Network).
no code implementations • Findings (ACL) 2022 • Zhuocheng Gong, Di He, Yelong Shen, Tie-Yan Liu, Weizhu Chen, Dongyan Zhao, Ji-Rong Wen, Rui Yan
Empirically, we show that (a) the dominant winning ticket can achieve performance that is comparable with that of the full-parameter model, (b) the dominant winning ticket is transferable across different tasks, (c) and the dominant winning ticket has a natural structure within each parameter matrix.
1 code implementation • EMNLP 2020 • Wanwei He, Min Yang, Rui Yan, Chengming Li, Ying Shen, Ruifeng Xu
Instead of adopting the classic student-teacher learning of forcing the output of a student network to exactly mimic the soft targets produced by the teacher networks, we introduce two discriminators as in generative adversarial network (GAN) to transfer knowledge from two teachers to the student.
Ranked #5 on Task-Oriented Dialogue Systems on KVRET
Generative Adversarial Network Task-Oriented Dialogue Systems
no code implementations • ACL 2022 • Tingchen Fu, Xueliang Zhao, Chongyang Tao, Ji-Rong Wen, Rui Yan
Knowledge-grounded conversation (KGC) shows great potential in building an engaging and knowledgeable chatbot, and knowledge selection is a key ingredient in it.
no code implementations • ACL 2022 • Chang Liu, Xu Tan, Chongyang Tao, Zhenxin Fu, Dongyan Zhao, Tie-Yan Liu, Rui Yan
To enable the chatbot to foresee the dialogue future, we design a beam-search-like roll-out strategy for dialogue future simulation using a typical dialogue generation model and a dialogue selector.
no code implementations • CCL 2020 • Lin Wang, Juntao Li, Rui Yan, Dongyan Zhao
Story generation is a challenging task of automatically creating natural languages to describe a sequence of events, which requires outputting text with not only a consistent topic but also novel wordings.
1 code implementation • ECCV 2020 • Yongqiang Mou, Lei Tan, Hui Yang, Jingying Chen, Leyuan Liu, Rui Yan, Yaohong Huang
In this paper, we address the problem of recognizing degradation images that are suffering from high blur or low-resolution.
no code implementations • 24 Apr 2024 • Xin Jiang, Hao Tang, Rui Yan, Jinhui Tang, Zechao Li
This paper presents a meticulous analysis leading to the proposal of practical guidelines to identify subcategory-specific discrepancies and generate discriminative features to design effective FGIR models.
no code implementations • 24 Apr 2024 • Lang Qin, ZiMing Wang, Runhao Jiang, Rui Yan, Huajin Tang
Spiking neural networks (SNNs) are widely applied in various fields due to their energy-efficient and fast-inference capabilities.
no code implementations • 16 Apr 2024 • Rui Yan, Gabriel Santos, Gethin Norman, David Parker, Marta Kwiatkowska
For the partially-informed agent, we propose a continual resolving approach which uses lower bounds, pre-computed offline with heuristic search value iteration (HSVI), instead of opponent counterfactual values.
1 code implementation • 28 Mar 2024 • Ang Lv, Kaiyi Zhang, Yuhan Chen, Yulong Wang, Lifeng Liu, Ji-Rong Wen, Jian Xie, Rui Yan
In this paper, we deeply explore the mechanisms employed by Transformer-based language models in factual recall tasks.
no code implementations • 22 Mar 2024 • Xiaoqing Zhang, Xiuying Chen, Shen Gao, Shuqi Li, Xin Gao, Ji-Rong Wen, Rui Yan
Given the user query, the information-seeking dialogue systems first retrieve a subset of response candidates, then further select the best response from the candidate set through re-ranking.
no code implementations • 18 Mar 2024 • Jinpeng Li, Zekai Zhang, Quan Tu, Xin Cheng, Dongyan Zhao, Rui Yan
Furthermore, although many prompt-based methods have been proposed to accomplish specific tasks, their performance in complex real-world scenarios involving a wide variety of dialog styles further enhancement.
no code implementations • 14 Mar 2024 • YuHan Liu, Xiuying Chen, Xiaoqing Zhang, Xing Gao, Ji Zhang, Rui Yan
Our simulation results uncover patterns in fake news propagation related to topic relevance, and individual traits, aligning with real-world observations.
no code implementations • 13 Mar 2024 • Jia-Nan Li, Quan Tu, Cunli Mao, Zhengtao Yu, Ji-Rong Wen, Rui Yan
Accordingly, we introduce StreamingDialogue, which compresses long dialogue history into conv-attn sinks with minimal losses, and thus reduces computational complexity quadratically with the number of sinks (i. e., the number of utterances).
no code implementations • 11 Mar 2024 • Zhuocheng Gong, Jiahao Liu, Jingang Wang, Xunliang Cai, Dongyan Zhao, Rui Yan
Our findings reveal several connections between the properties of perturbations and LLM performance, providing insights into the failure cases of uniform quantization and suggesting potential solutions to improve the robustness of LLM quantization.
no code implementations • 10 Mar 2024 • Rui Yan, Shuai Mi, Xiaoming Duan, Jintao Chen, Xiangyang Ji
The pursuers cooperate to protect a convex region from the evaders who try to reach the region.
no code implementations • 8 Mar 2024 • Hongda Sun, Yuxuan Liu, ChengWei Wu, Haiyu Yan, Cheng Tai, Xin Gao, Shuo Shang, Rui Yan
Open-domain question answering (ODQA) has emerged as a pivotal research spotlight in information systems.
no code implementations • 5 Mar 2024 • Chuanqi Cheng, Quan Tu, Wei Wu, Shuo Shang, Cunli Mao, Zhengtao Yu, Rui Yan
Personalized dialogue systems have gained significant attention in recent years for their ability to generate responses in alignment with different personas.
1 code implementation • 4 Mar 2024 • Changyu Chen, Xiting Wang, Ting-En Lin, Ang Lv, Yuchuan Wu, Xin Gao, Ji-Rong Wen, Rui Yan, Yongbin Li
In reasoning tasks, even a minor error can cascade into inaccurate results, leading to suboptimal performance of large language models in such domains.
2 code implementations • 3 Mar 2024 • Qizhi Pei, Lijun Wu, Kaiyuan Gao, Jinhua Zhu, Yue Wang, Zun Wang, Tao Qin, Rui Yan
The integration of biomolecular modeling with natural language (BL) has emerged as a promising interdisciplinary area at the intersection of artificial intelligence, chemistry and biology.
1 code implementation • 27 Feb 2024 • Qizhi Pei, Lijun Wu, Kaiyuan Gao, Xiaozhuan Liang, Yin Fang, Jinhua Zhu, Shufang Xie, Tao Qin, Rui Yan
However, previous efforts like BioT5 faced challenges in generalizing across diverse tasks and lacked a nuanced understanding of molecular structures, particularly in their textual representations (e. g., IUPAC).
Ranked #1 on Molecule Captioning on ChEBI-20
1 code implementation • 12 Jan 2024 • Kaiyi Zhang, Ang Lv, Yuhan Chen, Hansen Ha, Tao Xu, Rui Yan
In this paper, by treating in-context learning (ICL) as a meta-optimization process, we explain why LLMs are sensitive to the order of ICL examples.
1 code implementation • 2 Jan 2024 • Quan Tu, Shilong Fan, Zihang Tian, Rui Yan
Recently, the advent of large language models (LLMs) has revolutionized generative agents.
1 code implementation • 22 Dec 2023 • Hongda Sun, Hongzhan Lin, Rui Yan
Furthermore, we propose to generate medical reports to add textual descriptions for each medical event, providing broader applications for synthesized EHR data.
no code implementations • 22 Dec 2023 • Yin Luo, Qingchao Kong, Nan Xu, Jia Cao, Bao Hao, Baoyu Qu, Bo Chen, Chao Zhu, Chenyang Zhao, Donglei Zhang, Fan Feng, Feifei Zhao, Hailong Sun, Hanxuan Yang, Haojun Pan, Hongyu Liu, Jianbin Guo, Jiangtao Du, Jingyi Wang, Junfeng Li, Lei Sun, Liduo Liu, Lifeng Dong, Lili Liu, Lin Wang, Liwen Zhang, Minzheng Wang, Pin Wang, Ping Yu, Qingxiao Li, Rui Yan, Rui Zou, Ruiqun Li, Taiwen Huang, Xiaodong Wang, Xiaofei Wu, Xin Peng, Xina Zhang, Xing Fang, Xinglin Xiao, Yanni Hao, Yao Dong, Yigang Wang, Ying Liu, Yongyu Jiang, Yungan Wang, Yuqi Wang, Zhangsheng Wang, Zhaoxin Yu, Zhen Luo, Wenji Mao, Lei Wang, Dajun Zeng
As the latest advancements in natural language processing, large language models (LLMs) have achieved human-level language understanding and generation abilities in many real-world tasks, and even have been regarded as a potential path to the artificial general intelligence.
1 code implementation • 7 Dec 2023 • Yuhan Chen, Ang Lv, Ting-En Lin, Changyu Chen, Yuchuan Wu, Fei Huang, Yongbin Li, Rui Yan
Specifically, the crucial information in the context will be potentially overlooked by model when it is positioned in the trough zone of the attention waveform, leading to decreased performance.
Ranked #2 on Trajectory Planning on ToolBench
1 code implementation • 13 Nov 2023 • Ang Lv, Kaiyi Zhang, Shufang Xie, Quan Tu, Yuhan Chen, Ji-Rong Wen, Rui Yan
Recent studies have highlighted a phenomenon in large language models (LLMs) known as "the reversal curse," in which the order of knowledge entities in the training data biases the models' comprehension.
no code implementations • 4 Nov 2023 • Rui Yan, Xiaoming Duan, Rui Zou, Xin He, Zongying Shi, Francesco Bullo
We propose a cooperative strategy for the pursuers based on subgames for multiple pursuers against one evader and optimal task allocation.
no code implementations • 30 Oct 2023 • Zhuocheng Gong, Jiahao Liu, Qifan Wang, Jingang Wang, Xunliang Cai, Dongyan Zhao, Rui Yan
The effectiveness of ICL can be attributed to the strong language modeling capabilities of large language models (LLMs), which enable them to learn the mapping between input and labels based on in-context demonstrations.
1 code implementation • 28 Oct 2023 • Hongda Sun, Weikai Xu, Wei Liu, Jian Luan, Bin Wang, Shuo Shang, Ji-Rong Wen, Rui Yan
To address these challenges, we propose DetermLR, a novel reasoning framework that formulates the reasoning process as a transformational journey from indeterminate premises to determinate ones.
no code implementations • 25 Oct 2023 • Jixiang Hong, Quan Tu, Changyu Chen, Xing Gao, Ji Zhang, Rui Yan
With in-context learning (ICL) as the core of the cycle, the black-box models are able to rank the model-generated responses guided by human-craft instruction and demonstrations about their preferences.
no code implementations • 24 Oct 2023 • Jiduan Liu, Jiahao Liu, Qifan Wang, Jingang Wang, Xunliang Cai, Dongyan Zhao, Ran Lucien Wang, Rui Yan
In particular, our approach extracts knowledge from LLMs to construct a knowledge store, from which the small-scale model can retrieve relevant information and leverage it for effective inference.
no code implementations • 17 Oct 2023 • Rui Yan, Gabriel Santos, Gethin Norman, David Parker, Marta Kwiatkowska
Stochastic games are a well established model for multi-agent sequential decision making under uncertainty.
1 code implementation • 11 Oct 2023 • Qizhi Pei, Wei zhang, Jinhua Zhu, Kehan Wu, Kaiyuan Gao, Lijun Wu, Yingce Xia, Rui Yan
Recent advancements in biological research leverage the integration of molecules, proteins, and natural language to enhance drug discovery.
Ranked #2 on Text-based de novo Molecule Generation on ChEBI-20
1 code implementation • NeurIPS 2023 • Qizhi Pei, Kaiyuan Gao, Lijun Wu, Jinhua Zhu, Yingce Xia, Shufang Xie, Tao Qin, Kun He, Tie-Yan Liu, Rui Yan
In this work, we propose $\mathbf{FABind}$, an end-to-end model that combines pocket prediction and docking to achieve accurate and fast protein-ligand binding.
1 code implementation • 29 Sep 2023 • Xin Cheng, Xun Wang, Tao Ge, Si-Qing Chen, Furu Wei, Dongyan Zhao, Rui Yan
In this paper, we introduce SCALE, a collaborative framework that connects compact Specialized Translation Models (STMs) and general-purpose Large Language Models (LLMs) as one unified translation engine.
no code implementations • 14 Sep 2023 • David Junhao Zhang, Heng Wang, Chuhui Xue, Rui Yan, Wenqing Zhang, Song Bai, Mike Zheng Shou
Dataset condensation aims to condense a large dataset with a lot of training samples into a small set.
no code implementations • 11 Sep 2023 • Huajin Tang, Pengjie Gu, Jayawan Wijekoon, MHD Anas Alsakkal, ZiMing Wang, Jiangrong Shen, Rui Yan
Neuromorphic computing holds the promise to achieve the energy efficiency and robust learning performance of biological neural systems.
no code implementations • 29 Aug 2023 • Henghao Zhao, Kevin Qinghong Lin, Rui Yan, Zechao Li
An arbitrary setting can be used in DiffusionVMR during inference without consistency with the training phase.
Ranked #4 on Video Grounding on QVHighlights
1 code implementation • 20 Aug 2023 • Quan Tu, Chuanqi Chen, Jinpeng Li, Yanran Li, Shuo Shang, Dongyan Zhao, Ran Wang, Rui Yan
In our modern, fast-paced, and interconnected world, the importance of mental well-being has grown into a matter of great urgency.
no code implementations • 6 Aug 2023 • Hao Tang, Jun Liu, Shuanglin Yan, Rui Yan, Zechao Li, Jinhui Tang
Due to the scarcity of manually annotated data required for fine-grained video understanding, few-shot fine-grained (FS-FG) action recognition has gained significant attention, with the aim of classifying novel fine-grained action categories with only a few labeled instances.
1 code implementation • ICCV 2023 • Kevin Qinghong Lin, Pengchuan Zhang, Joya Chen, Shraman Pramanick, Difei Gao, Alex Jinpeng Wang, Rui Yan, Mike Zheng Shou
Most methods in this direction develop taskspecific models that are trained with type-specific labels, such as moment retrieval (time interval) and highlight detection (worthiness curve), which limits their abilities to generalize to various VTG tasks and labels.
Ranked #3 on Natural Language Moment Retrieval on TACoS
no code implementations • 20 Jul 2023 • Yingpeng Du, Di Luo, Rui Yan, Hongzhi Liu, Yang song, HengShu Zhu, Jie Zhang
However, directly leveraging LLMs to enhance recommendation results is not a one-size-fits-all solution, as LLMs may suffer from fabricated generation and few-shot problems, which degrade the quality of resume completion.
1 code implementation • 2 Jul 2023 • Quan Tu, Shen Gao, Xiaolong Wu, Zhao Cao, Ji-Rong Wen, Rui Yan
Conversational search has been regarded as the next-generation search paradigm.
no code implementations • 30 Jun 2023 • Rui Yan, Gabriel Santos, Gethin Norman, David Parker, Marta Kwiatkowska
This requires functions over continuous-state beliefs, for which we propose a novel piecewise linear and convex representation (P-PWLC) in terms of polyhedra covering the continuous-state space and value vectors, and extend Bellman backups to this representation.
no code implementations • 29 Jun 2023 • Ang Lv, Jinpeng Li, Yuhan Chen, Xing Gao, Ji Zhang, Rui Yan
In open-domain dialogue generation tasks, contexts and responses in most datasets are one-to-one mapped, violating an important many-to-many characteristic: a context leads to various responses, and a response answers multiple contexts.
no code implementations • NeurIPS 2023 • Gehua Ma, Runhao Jiang, Rui Yan, Huajin Tang
This work presents the temporal conditioning spiking latent variable models (TeCoS-LVM) to simulate the neural response to natural visual stimuli.
1 code implementation • 16 Jun 2023 • Changyu Chen, Xiting Wang, Yiqiao Jin, Victor Ye Dong, Li Dong, Jie Cao, Yi Liu, Rui Yan
In reinforcement learning (RL), there are two major settings for interacting with the environment: online and offline.
no code implementations • 7 Jun 2023 • Shufang Xie, Rui Yan, Junliang Guo, Yingce Xia, Lijun Wu, Tao Qin
Furthermore, we propose a lightweight adapter to adjust the weights when combing neural network and KNN predictions conditioned on the hidden representation and the retrieved templates.
no code implementations • 30 May 2023 • Zhuocheng Gong, Jiahao Liu, Qifan Wang, Yang Yang, Jingang Wang, Wei Wu, Yunsen Xian, Dongyan Zhao, Rui Yan
While transformer-based pre-trained language models (PLMs) have dominated a number of NLP applications, these models are heavy to deploy and expensive to use.
1 code implementation • 26 May 2023 • Jiduan Liu, Jiahao Liu, Qifan Wang, Jingang Wang, Wei Wu, Yunsen Xian, Dongyan Zhao, Kai Chen, Rui Yan
In this paper, we propose a novel approach, RankCSE, for unsupervised sentence representation learning, which incorporates ranking consistency and ranking distillation with contrastive learning into a unified framework.
1 code implementation • 25 May 2023 • Gehua Ma, Rui Yan, Huajin Tang
Despite extensive research on spiking neural networks (SNNs), most studies are established on deterministic models, overlooking the inherent non-deterministic, noisy nature of neural computations.
no code implementations • NeurIPS 2023 • Shuzheng Si, Wentao Ma, Haoyu Gao, Yuchuan Wu, Ting-En Lin, Yinpei Dai, Hangyu Li, Rui Yan, Fei Huang, Yongbin Li
SpokenWOZ further incorporates common spoken characteristics such as word-by-word processing and reasoning in spoken language.
1 code implementation • 19 May 2023 • Xin Cheng, Yankai Lin, Xiuying Chen, Dongyan Zhao, Rui Yan
The key intuition is to decouple the knowledge storage from model parameters with an editable and scalable key-value memory and leverage knowledge in an explainable manner by knowledge retrieval in the DPM.
1 code implementation • 18 May 2023 • Ang Lv, Xu Tan, Peiling Lu, Wei Ye, Shikun Zhang, Jiang Bian, Rui Yan
Our proposed representation, coupled with the non-autoregressive generative model, empowers GETMusic to generate music with any arbitrary source-target track combinations.
1 code implementation • 3 May 2023 • Xin Cheng, Di Luo, Xiuying Chen, Lemao Liu, Dongyan Zhao, Rui Yan
In this paper, by exploring the duality of the primal problem: better generation also prompts better memory, we propose a novel framework, selfmem, which addresses this limitation by iteratively employing a retrieval-augmented generator to create an unbounded memory pool and using a memory selector to choose one output as memory for the subsequent generation round.
1 code implementation • 28 Apr 2023 • Shufang Xie, Huishuai Zhang, Junliang Guo, Xu Tan, Jiang Bian, Hany Hassan Awadalla, Arul Menezes, Tao Qin, Rui Yan
In this paper, we propose ResiDual, a novel Transformer architecture with Pre-Post-LN (PPLN), which fuses the connections in Post-LN and Pre-LN together and inherits their advantages while avoids their limitations.
no code implementations • 21 Apr 2023 • Houcheng Su, Jintao Huang, Daixian Liu, Rui Yan, Jiao Li, Chi-Man Vong
Multi-instance multi-label (MIML) learning is widely applicated in numerous domains, such as the image classification where one image contains multiple instances correlated with multiple logic labels simultaneously.
no code implementations • 8 Apr 2023 • Binqian Xu, Xiangbo Shu, Rui Yan, Guo-Sen Xie, Yixiao Ge, Mike Zheng Shou
In particular, we propose a novel Attack-Augmentation Mixing-Contrastive learning (A$^2$MC) to contrast hard positive features and hard negative features for learning more robust skeleton representations.
no code implementations • 17 Mar 2023 • Xiuying Chen, Mingzhe Li, Jiayi Zhang, Xiaoqiang Xia, Chen Wei, Jianwei Cui, Xin Gao, Xiangliang Zhang, Rui Yan
As it is cumbersome and expensive to acquire a huge amount of data for training neural dialog models, data augmentation is proposed to effectively utilize existing training samples.
no code implementations • 27 Jan 2023 • Xin Cheng, Shen Gao, Yuchi Zhang, Yongliang Wang, Xiuying Chen, Mingzhe Li, Dongyan Zhao, Rui Yan
Review summarization is a non-trivial task that aims to summarize the main idea of the product review in the E-commerce website.
no code implementations • 3 Jan 2023 • Mingzhe Li, Xiuying Chen, Weiheng Liao, Yang song, Tao Zhang, Dongyan Zhao, Rui Yan
The key idea is to reduce the number of parameters that rely on interview dialogs by disentangling the knowledge selector and dialog generator so that most parameters can be trained with ungrounded dialogs as well as the resume data that are not low-resource.
1 code implementation • 2 Jan 2023 • Xiuying Chen, Mingzhe Li, Shen Gao, Zhangming Chan, Dongyan Zhao, Xin Gao, Xiangliang Zhang, Rui Yan
Nowadays, time-stamped web documents related to a general news query floods spread throughout the Internet, and timeline summarization targets concisely summarizing the evolution trajectory of events along the timeline.
no code implementations • 8 Dec 2022 • Xiuying Chen, Mingzhe Li, Shen Gao, Rui Yan, Xin Gao, Xiangliang Zhang
We first propose a Multi-granularity Unsupervised Summarization model (MUS) as a simple and low-cost solution to the task.
1 code implementation • 6 Dec 2022 • Xin Cheng, Shen Gao, Lemao Liu, Dongyan Zhao, Rui Yan
Retrieval-augmented Neural Machine Translation models have been successful in many translation scenarios.
1 code implementation • 21 Nov 2022 • Lang Qin, Rui Yan, Huajin Tang
In recent years, spiking neural networks (SNNs) have been used in reinforcement learning (RL) due to their low power consumption and event-driven features.
no code implementations • 22 Oct 2022 • Xueliang Zhao, Tingchen Fu, Chongyang Tao, Rui Yan
Knowledge-grounded conversation (KGC) shows excellent potential to deliver an engaging and informative response.
no code implementations • 22 Oct 2022 • Xueliang Zhao, Lemao Liu, Tingchen Fu, Shuming Shi, Dongyan Zhao, Rui Yan
With the availability of massive general-domain dialogue data, pre-trained dialogue generation appears to be super appealing to transfer knowledge from the general domain to downstream applications.
1 code implementation • 11 Aug 2022 • Ang Lv, Xu Tan, Tao Qin, Tie-Yan Liu, Rui Yan
These characteristics cannot be well handled by neural generation models that learn lyric-to-melody mapping in an end-to-end way, due to several issues: (1) lack of aligned lyric-melody training data to sufficiently learn lyric-melody feature alignment; (2) lack of controllability in generation to better and explicitly align the lyric-melody features.
1 code implementation • IEEE Transactions on Cybernetics 2022 • Chenxiang Ma, Rui Yan, Zhaofei Yu, Qiang Yu
We then propose two variants that additionally incorporate temporal dependencies through a backward and forward process, respectively.
1 code implementation • 4 Jul 2022 • Kevin Qinghong Lin, Alex Jinpeng Wang, Mattia Soldan, Michael Wray, Rui Yan, Eric Zhongcong Xu, Difei Gao, RongCheng Tu, Wenzhe Zhao, Weijie Kong, Chengfei Cai, Hongfa Wang, Dima Damen, Bernard Ghanem, Wei Liu, Mike Zheng Shou
In this report, we propose a video-language pretraining (VLP) based solution \cite{kevin2022egovlp} for four Ego4D challenge tasks, including Natural Language Query (NLQ), Moment Query (MQ), Object State Change Classification (OSCC), and PNR Localization (PNR).
1 code implementation • 4 Jul 2022 • Kevin Qinghong Lin, Alex Jinpeng Wang, Rui Yan, Eric Zhongcong Xu, RongCheng Tu, Yanru Zhu, Wenzhe Zhao, Weijie Kong, Chengfei Cai, Hongfa Wang, Wei Liu, Mike Zheng Shou
In this report, we propose a video-language pretraining (VLP) based solution \cite{kevin2022egovlp} for the EPIC-KITCHENS-100 Multi-Instance Retrieval (MIR) challenge.
1 code implementation • 23 Jun 2022 • Shufang Xie, Rui Yan, Peng Han, Yingce Xia, Lijun Wu, Chenjuan Guo, Bin Yang, Tao Qin
We observe that the same intermediate molecules are visited many times in the searching process, and they are usually independently treated in previous tree-based methods (e. g., AND-OR tree search, Monte Carlo tree search).
Ranked #2 on Multi-step retrosynthesis on USPTO-190
2 code implementations • 20 Jun 2022 • Qizhi Pei, Lijun Wu, Jinhua Zhu, Yingce Xia, Shufang Xie, Tao Qin, Haiguang Liu, Tie-Yan Liu, Rui Yan
Accurate prediction of Drug-Target Affinity (DTA) is of vital importance in early-stage drug discovery, facilitating the identification of drugs that can effectively interact with specific targets and regulate their activities.
Ranked #1 on Drug Discovery on KIBA
2 code implementations • 3 Jun 2022 • Kevin Qinghong Lin, Alex Jinpeng Wang, Mattia Soldan, Michael Wray, Rui Yan, Eric Zhongcong Xu, Difei Gao, RongCheng Tu, Wenzhe Zhao, Weijie Kong, Chengfei Cai, Hongfa Wang, Dima Damen, Bernard Ghanem, Wei Liu, Mike Zheng Shou
Video-Language Pretraining (VLP), which aims to learn transferable representation to advance a wide range of video-text downstream tasks, has recently received increasing attention.
1 code implementation • 26 May 2022 • Xiuying Chen, Hind Alamro, Mingzhe Li, Shen Gao, Rui Yan, Xin Gao, Xiangliang Zhang
The related work section is an important component of a scientific paper, which highlights the contribution of the target paper in the context of the reference papers.
no code implementations • ACL 2022 • Mingzhe Li, Xiexiong Lin, Xiuying Chen, Jinxiong Chang, Qishen Zhang, Feng Wang, Taifeng Wang, Zhongyi Liu, Wei Chu, Dongyan Zhao, Rui Yan
Contrastive learning has achieved impressive success in generation tasks to militate the "exposure bias" problem and discriminatively exploit the different quality of references.
1 code implementation • 17 May 2022 • Rui Yan, Liangqiong Qu, Qingyue Wei, Shih-Cheng Huang, Liyue Shen, Daniel Rubin, Lei Xing, Yuyin Zhou
The collection and curation of large-scale medical datasets from multiple institutions is essential for training accurate deep learning models, but privacy concerns often hinder data sharing.
1 code implementation • 16 May 2022 • ZiMing Wang, Shuang Lian, Yuhao Zhang, Xiaoxin Cui, Rui Yan, Huajin Tang
By evaluating on challenging datasets including CIFAR-10, CIFAR- 100 and ImageNet, the proposed method demonstrates the state-of-the-art performance in terms of accuracy, latency and energy preservation.
1 code implementation • Findings (NAACL) 2022 • Peggy Tang, Kun Hu, Rui Yan, Lei Zhang, Junbin Gao, Zhiyong Wang
Optimal sentence extraction is conceptualised as obtaining an optimal summary that minimises the transportation cost to a given document regarding their semantic distributions.
no code implementations • 19 Apr 2022 • Rui Yan, Cheng Wen, Shuran Zhou, Tingwei Guo, Wei Zou, Xiangang Li
This paper describes our best system and methodology for ADD 2022: The First Audio Deep Synthesis Detection Challenge\cite{Yi2022ADD}.
no code implementations • 19 Apr 2022 • Cheng Wen, Tingwei Guo, Xingjun Tan, Rui Yan, Shuran Zhou, Chuandong Xie, Wei Zou, Xiangang Li
In this paper, we describe our speech generation system for the first Audio Deep Synthesis Detection Challenge (ADD 2022).
no code implementations • 18 Apr 2022 • Jiduan Liu, Jiahao Liu, Yang Yang, Jingang Wang, Wei Wu, Dongyan Zhao, Rui Yan
To enhance the performance of dense retrieval models without loss of efficiency, we propose a GNN-encoder model in which query (passage) information is fused into passage (query) representations via graph neural networks that are constructed by queries and their top retrieved passages.
no code implementations • NAACL 2022 • Xueliang Zhao, Tingchen Fu, Chongyang Tao, Wei Wu, Dongyan Zhao, Rui Yan
Grounding dialogue generation by extra knowledge has shown great potentials towards building a system capable of replying with knowledgeable and engaging responses.
1 code implementation • 6 Apr 2022 • Tingchen Fu, Xueliang Zhao, Chongyang Tao, Ji-Rong Wen, Rui Yan
In this work, we introduce personal memory into knowledge selection in KGC to address the personalization issue.
no code implementations • CVPR 2022 • Mingfei Han, David Junhao Zhang, Yali Wang, Rui Yan, Lina Yao, Xiaojun Chang, Yu Qiao
Learning spatial-temporal relation among multiple actors is crucial for group activity recognition.
no code implementations • 26 Mar 2022 • Sha Yuan, Hanyu Zhao, Shuai Zhao, Jiahong Leng, Yangxiao Liang, Xiaozhi Wang, Jifan Yu, Xin Lv, Zhou Shao, Jiaao He, Yankai Lin, Xu Han, Zhenghao Liu, Ning Ding, Yongming Rao, Yizhao Gao, Liang Zhang, Ming Ding, Cong Fang, Yisen Wang, Mingsheng Long, Jing Zhang, Yinpeng Dong, Tianyu Pang, Peng Cui, Lingxiao Huang, Zheng Liang, HuaWei Shen, HUI ZHANG, Quanshi Zhang, Qingxiu Dong, Zhixing Tan, Mingxuan Wang, Shuo Wang, Long Zhou, Haoran Li, Junwei Bao, Yingwei Pan, Weinan Zhang, Zhou Yu, Rui Yan, Chence Shi, Minghao Xu, Zuobai Zhang, Guoqiang Wang, Xiang Pan, Mengjie Li, Xiaoyu Chu, Zijun Yao, Fangwei Zhu, Shulin Cao, Weicheng Xue, Zixuan Ma, Zhengyan Zhang, Shengding Hu, Yujia Qin, Chaojun Xiao, Zheni Zeng, Ganqu Cui, Weize Chen, Weilin Zhao, Yuan YAO, Peng Li, Wenzhao Zheng, Wenliang Zhao, Ziyi Wang, Borui Zhang, Nanyi Fei, Anwen Hu, Zenan Ling, Haoyang Li, Boxi Cao, Xianpei Han, Weidong Zhan, Baobao Chang, Hao Sun, Jiawen Deng, Chujie Zheng, Juanzi Li, Lei Hou, Xigang Cao, Jidong Zhai, Zhiyuan Liu, Maosong Sun, Jiwen Lu, Zhiwu Lu, Qin Jin, Ruihua Song, Ji-Rong Wen, Zhouchen Lin, LiWei Wang, Hang Su, Jun Zhu, Zhifang Sui, Jiajun Zhang, Yang Liu, Xiaodong He, Minlie Huang, Jian Tang, Jie Tang
With the rapid development of deep learning, training Big Models (BMs) for multiple downstream tasks becomes a popular paradigm.
1 code implementation • ACL 2022 • Quan Tu, Yanran Li, Jianwei Cui, Bin Wang, Ji-Rong Wen, Rui Yan
Applying existing methods to emotional support conversation -- which provides valuable assistance to people who are in need -- has two major limitations: (a) they generally employ a conversation-level emotion label, which is too coarse-grained to capture user's instant mental state; (b) most of them focus on expressing empathy in the response(s) rather than gradually reducing user's distress.
2 code implementations • 15 Mar 2022 • Guanyu Cai, Yixiao Ge, Binjie Zhang, Alex Jinpeng Wang, Rui Yan, Xudong Lin, Ying Shan, Lianghua He, XiaoHu Qie, Jianping Wu, Mike Zheng Shou
Recent dominant methods for video-language pre-training (VLP) learn transferable representations from the raw pixels in an end-to-end manner to achieve advanced performance on downstream video-language retrieval.
1 code implementation • CVPR 2023 • Alex Jinpeng Wang, Yixiao Ge, Rui Yan, Yuying Ge, Xudong Lin, Guanyu Cai, Jianping Wu, Ying Shan, XiaoHu Qie, Mike Zheng Shou
In this work, we for the first time introduce an end-to-end video-language model, namely \textit{all-in-one Transformer}, that embeds raw video and textual signals into joint representations using a unified backbone architecture.
Ranked #6 on TGIF-Transition on TGIF-QA (using extra training data)
no code implementations • 13 Feb 2022 • Rui Yan, Gabriel Santos, Gethin Norman, David Parker, Marta Kwiatkowska
Second, we introduce two novel representations for the value functions and strategies, constant-piecewise-linear (CON-PWL) and constant-piecewise-constant (CON-PWC) respectively, and propose Minimax-action-free PI by extending a recent PI method based on alternating player choices for finite state spaces to Borel state spaces, which does not require normal-form games to be solved.
1 code implementation • 27 Dec 2021 • Shen Gao, Yuchi Zhang, Yongliang Wang, Yang Dong, Xiuying Chen, Dongyan Zhao, Rui Yan
Most of the CQA methods only incorporate articles or Wikipedia to extract knowledge and answer the user's question.
no code implementations • 27 Dec 2021 • Yuan YAO, Qingxiu Dong, Jian Guan, Boxi Cao, Zhengyan Zhang, Chaojun Xiao, Xiaozhi Wang, Fanchao Qi, Junwei Bao, Jinran Nie, Zheni Zeng, Yuxian Gu, Kun Zhou, Xuancheng Huang, Wenhao Li, Shuhuai Ren, Jinliang Lu, Chengqiang Xu, Huadong Wang, Guoyang Zeng, Zile Zhou, Jiajun Zhang, Juanzi Li, Minlie Huang, Rui Yan, Xiaodong He, Xiaojun Wan, Xin Zhao, Xu sun, Yang Liu, Zhiyuan Liu, Xianpei Han, Erhong Yang, Zhifang Sui, Maosong Sun
We argue that for general-purpose language intelligence evaluation, the benchmark itself needs to be comprehensive and systematic.
no code implementations • 21 Dec 2021 • Xiangbo Shu, Jiawen Yang, Rui Yan, Yan Song
This work focuses on the task of elderly activity recognition, which is a challenging task due to the existence of individual actions and human-object interactions in elderly activities.
1 code implementation • 2 Dec 2021 • Rui Yan, Mike Zheng Shou, Yixiao Ge, Alex Jinpeng Wang, Xudong Lin, Guanyu Cai, Jinhui Tang
Video-Text pre-training aims at learning transferable representations from large-scale video-text pairs via aligning the semantics between visual and textual information.
1 code implementation • NeurIPS 2021 • Jinpeng Li, Yingce Xia, Rui Yan, Hongda Sun, Dongyan Zhao, Tie-Yan Liu
Considering there is no parallel data between the contexts and the responses of target style S1, existing works mainly use back translation to generate stylized synthetic data for training, where the data about context, target style S1 and an intermediate style S0 is used.
1 code implementation • CVPR 2022 • Alex Jinpeng Wang, Yixiao Ge, Guanyu Cai, Rui Yan, Xudong Lin, Ying Shan, XiaoHu Qie, Mike Zheng Shou
In this work, we present Object-aware Transformers, an object-centric approach that extends video-language transformer to incorporate object representations.
Ranked #20 on Zero-Shot Video Retrieval on DiDeMo
no code implementations • 5 Nov 2021 • JianPing Mei, Yilun Zheng, Qianwei Zhou, Rui Yan
In this paper, we study the multi-task sentiment classification problem in the continual learning setting, i. e., a model is sequentially trained to classifier the sentiment of reviews of products in a particular category.
1 code implementation • ICLR 2022 • Shufang Xie, Ang Lv, Yingce Xia, Lijun Wu, Tao Qin, Rui Yan, Tie-Yan Liu
Autoregressive sequence generation, a prevalent task in machine learning and natural language processing, generates every target token conditioned on both a source input and previously generated target tokens.
1 code implementation • ACM Transactions on Information Systems 2021 • Ruijian Xu, Chongyang Tao, Jiazhan Feng, Wei Wu, Rui Yan, Dongyan Zhao
To tackle these challenges, we propose a representation[K]-interaction[L]-matching framework that explores multiple types of deep interactive representations to build context-response matching models for response selection.
no code implementations • ACL 2021 • Chongyang Tao, Changyu Chen, Jiazhan Feng, Ji-Rong Wen, Rui Yan
Recently, many studies are emerging towards building a retrieval-based dialogue system that is able to effectively leverage background knowledge (e. g., documents) when conversing with humans.
1 code implementation • ACL 2021 • Xiuying Chen, Hind Alamro, Mingzhe Li, Shen Gao, Xiangliang Zhang, Dongyan Zhao, Rui Yan
Hence, in this paper, we propose a Relation-aware Related work Generator (RRG), which generates an abstractive related work from the given multiple scientific papers in the same research area.
1 code implementation • 6 Jul 2021 • Lifa Zhu, Dongrui Liu, Changwei Lin, Rui Yan, Francisco Gómez-Fernández, Ninghua Yang, Ziyong Feng
3D point cloud registration is a fundamental task in robotics and computer vision.
no code implementations • NAACL 2021 • Chongyang Tao, Shen Gao, Juntao Li, Yansong Feng, Dongyan Zhao, Rui Yan
Sequential information, a. k. a., orders, is assumed to be essential for processing a sequence with recurrent neural network or convolutional neural network based encoders.
1 code implementation • 30 May 2021 • Shuhe Wang, Yuxian Meng, Xiaofei Sun, Fei Wu, Rongbin Ouyang, Rui Yan, Tianwei Zhang, Jiwei Li
Specifically, we propose to model the mutual dependency between text-visual features, where the model not only needs to learn the probability of generating the next dialog utterance given preceding dialog utterances and visual contexts, but also the probability of predicting the visual features in which a dialog utterance takes place, leading the generated dialog utterance specific to the visual context.
no code implementations • 17 Mar 2021 • Juntao Li, Chang Liu, Chongyang Tao, Zhangming Chan, Dongyan Zhao, Min Zhang, Rui Yan
To fill the gap between these up-to-date methods and the real-world applications, we incorporate user-specific dialogue history into the response selection and propose a personalized hybrid matching network (PHMN).
no code implementations • 10 Mar 2021 • Mingfei Guo, Xiuying Chen, Juntao Li, Dongyan Zhao, Rui Yan
Automatically identifying fake news from the Internet is a challenging problem in deception detection tasks.
1 code implementation • 30 Dec 2020 • Yuxian Meng, Shuhe Wang, Qinghong Han, Xiaofei Sun, Fei Wu, Rui Yan, Jiwei Li
Based on this dataset, we propose a family of encoder-decoder models leveraging both textual and visual contexts, from coarse-grained image features extracted from CNNs to fine-grained object features extracted from Faster R-CNNs.
no code implementations • 14 Dec 2020 • Mingzhe Li, Xiuying Chen, Min Yang, Shen Gao, Dongyan Zhao, Rui Yan
In this paper, we propose a Disentanglement-based Attractive Headline Generator (DAHG) that generates headline which captures the attractive content following the attractive style.
1 code implementation • 14 Dec 2020 • Xiuying Chen, Zhi Cui, Jiayi Zhang, Chen Wei, Jianwei Cui, Bin Wang, Dongyan Zhao, Rui Yan
Hence, in this paper, we propose to improve the response generation performance by examining the model's ability to answer a reading comprehension question, where the question is focused on the omitted information in the dialog.
1 code implementation • 10 Dec 2020 • Rui Yan, Lingxi Xie, Xiangbo Shu, Jinhui Tang
To understand a complex action, multiple sources of information, including appearance, positional, and semantic features, need to be integrated.
no code implementations • 4 Dec 2020 • Xiangyi Cui, Zhou Wang, Yonglin Ju, Xiuli Wang, Huaxuan Liu, Wenbo Ma, Jianglai Liu, Li Zhao, Xiangdong Ji, Shuaijie Li, Rui Yan, Haidong Sha, Peiyao Huang
An online cryogenic distillation system for the removal of krypton and radon from xenon was designed and constructed for PandaX-4T, a highly sensitive dark matter detection experiment.
Instrumentation and Detectors High Energy Physics - Experiment
no code implementations • COLING 2020 • Wenpeng Hu, Ran Le, Bing Liu, Jinwen Ma, Dongyan Zhao, Rui Yan
Understanding neural models is a major topic of interest in the deep learning community.
1 code implementation • 23 Nov 2020 • Juntao Li, Ruidan He, Hai Ye, Hwee Tou Ng, Lidong Bing, Rui Yan
Experimental results show that our proposed method achieves significant performance improvements over the state-of-the-art pretrained cross-lingual language model in the CLCD setting.
no code implementations • 14 Nov 2020 • Shen Gao, Xiuying Chen, Zhaochun Ren, Dongyan Zhao, Rui Yan
To generate more meaningful answers, in this paper, we propose a novel generative neural model, called the Meaningful Product Answer Generator (MPAG), which alleviates the safe answer problem by taking product reviews, product attributes, and a prototype answer into consideration.
no code implementations • 5 Nov 2020 • Shen Gao, Xiuying Chen, Li Liu, Dongyan Zhao, Rui Yan
Hence, in this paper, we propose to recommend an appropriate sticker to user based on multi-turn dialog context and sticker using history of user.
1 code implementation • EMNLP 2020 • Xueliang Zhao, Wei Wu, Can Xu, Chongyang Tao, Dongyan Zhao, Rui Yan
We study knowledge-grounded dialogue generation with pre-trained language models.
1 code implementation • EMNLP 2020 • Mingzhe Li, Xiuying Chen, Shen Gao, Zhangming Chan, Dongyan Zhao, Rui Yan
Hence, in this paper, we propose the task of Video-based Multimodal Summarization with Multimodal Output (VMSMO) to tackle such a problem.
no code implementations • 14 Sep 2020 • Ruijian Xu, Chongyang Tao, Daxin Jiang, Xueliang Zhao, Dongyan Zhao, Rui Yan
To address these issues, in this paper, we propose learning a context-response matching model with auxiliary self-supervised tasks designed for the dialogue data based on pre-trained language models.
Ranked #4 on Conversational Response Selection on E-commerce
no code implementations • ECCV 2020 • Rui Yan, Lingxi Xie, Jinhui Tang, Xiangbo Shu, Qi Tian
This paper presents a new task named weakly-supervised group activity recognition (GAR) which differs from conventional GAR tasks in that only video-level labels are available, yet the important persons within each frame are not provided even in the training data.
no code implementations • 17 Jun 2020 • Rui Yan, Xiaoming Duan, Zongying Shi, Yisheng Zhong, Jason R. Marden, Francesco Bullo
With this knowledge we propose a class of perturbed SBRD with the following property: only policies with maximum metric are observed with nonzero probability for a broad class of stochastic games with finite memory.
Multi-agent Reinforcement Learning reinforcement-learning +1
no code implementations • 11 Jun 2020 • Pin Tang, Chen Zu, Mei Hong, Rui Yan, Xingchen Peng, Jianghong Xiao, Xi Wu, Jiliu Zhou, Luping Zhou, Yan Wang
In this paper, we propose a Dense SegU-net (DSU-net) framework for automatic NPC segmentation in MRI.
1 code implementation • 17 May 2020 • Juntao Li, Chang Liu, Jian Wang, Lidong Bing, Hongsong Li, Xiaozhong Liu, Dongyan Zhao, Rui Yan
We manually collect a new and high-quality paired dataset, where each pair contains an unordered product attribute set in the source language and an informative product description in the target language.
1 code implementation • CVPR 2020 • Yihui He, Rui Yan, Katerina Fragkiadaki, Shoou-I Yu
The intuition is: given a 2D location p in the current view, we would like to first find its corresponding point p' in a neighboring view, and then combine the features at p' with the features at p, thus leading to a 3D-aware feature at p. Inspired by stereo matching, the epipolar transformer leverages epipolar constraints and feature matching to approximate the features at p'.
Ranked #1 on 3D Hand Pose Estimation on InterHand2.6M
no code implementations • 10 May 2020 • Shen Gao, Xiuying Chen, Zhaochun Ren, Dongyan Zhao, Rui Yan
Text summarization is the research area aiming at creating a short and condensed version of the original document, which conveys the main idea of the document in a few words.
no code implementations • 30 Apr 2020 • Jiayi Zhang, Chongyang Tao, Zhenjing Xu, Qiaojing Xie, Wei Chen, Rui Yan
Aiming at generating responses that approximate the ground-truth and receive high ranking scores from the discriminator, the two generators learn to generate improved highly relevant responses and competitive unobserved candidates respectively, while the discriminative ranker is trained to identify true responses from adversarial ones, thus featuring the merits of both generator counterparts.
1 code implementation • 10 Mar 2020 • Shen Gao, Xiuying Chen, Chang Liu, Li Liu, Dongyan Zhao, Rui Yan
Stickers with vivid and engaging expressions are becoming increasingly popular in online messaging apps, and some works are dedicated to automatically select sticker response by matching text labels of stickers with previous utterances.
no code implementations • ICLR 2020 • Xueliang Zhao, Wei Wu, Chongyang Tao, Can Xu, Dongyan Zhao, Rui Yan
In such a low-resource setting, we devise a disentangled response decoder in order to isolate parameters that depend on knowledge-grounded dialogues from the entire generation model.
1 code implementation • 7 Nov 2019 • Zhenxin Fu, Feng Ji, Wenpeng Hu, Wei Zhou, Dongyan Zhao, Haiqing Chen, Rui Yan
Information-seeking conversation system aims at satisfying the information needs of users through conversations.
1 code implementation • COLING 2020 • Wenpeng Hu, Mengyu Wang, Bing Liu, Feng Ji, Haiqing Chen, Dongyan Zhao, Jinwen Ma, Rui Yan
The key idea of the proposed approach is to use a Forward Transformation to transform dense representations to sparse representations.
no code implementations • IJCNLP 2019 • Jia Li, Chongyang Tao, Wei Wu, Yansong Feng, Dongyan Zhao, Rui Yan
We study how to sample negative examples to automatically construct a training set for effective model learning in retrieval-based dialogue systems.
no code implementations • IJCNLP 2019 • Ran Le, Wenpeng Hu, Mingyue Shang, Zhenjun You, Lidong Bing, Dongyan Zhao, Rui Yan
Previous research on dialogue systems generally focuses on the conversation between two participants, yet multi-party conversations which involve more than two participants within one session bring up a more complicated but realistic scenario.
no code implementations • IJCNLP 2019 • Zhangming Chan, Juntao Li, Xiaopeng Yang, Xiuying Chen, Wenpeng Hu, Dongyan Zhao, Rui Yan
In this work, we improve the WAE for response generation.
no code implementations • IJCNLP 2019 • Zhangming Chan, Xiuying Chen, Yongliang Wang, Juntao Li, Zhiqiang Zhang, Kun Gai, Dongyan Zhao, Rui Yan
Different from other text generation tasks, in product description generation, it is of vital importance to generate faithful descriptions that stick to the product attribute information.
1 code implementation • ACL 2020 • Yiping Song, Zequn Liu, Wei Bi, Rui Yan, Ming Zhang
Training the generative models with minimal corpus is one of the critical challenges for building open-domain dialogue systems.
no code implementations • 28 Oct 2019 • Xiuying Chen, Daorui Xiao, Shen Gao, Guojun Liu, Wei. Lin, Bo Zheng, Dongyan Zhao, Rui Yan
Sponsored search optimizes revenue and relevance, which is estimated by Revenue Per Mille (RPM).
no code implementations • 6 Oct 2019 • Chen Chen, Lisong Qiu, Zhenxin Fu, Dongyan Zhao, Junfei Liu, Rui Yan
Existing dialog systems are all monolingual, where features shared among different languages are rarely explored.
no code implementations • 25 Sep 2019 • Wenpeng Hu, Ran Le, Bing Liu, Feng Ji, Haiqing Chen, Dongyan Zhao, Jinwen Ma, Rui Yan
Positive-unlabeled (PU) learning learns a binary classifier using only positive and unlabeled examples without labeled negative examples.
no code implementations • IJCNLP 2019 • Mingyue Shang, Piji Li, Zhenxin Fu, Lidong Bing, Dongyan Zhao, Shuming Shi, Rui Yan
Text style transfer task requires the model to transfer a sentence of one style to another style while retaining its original content meaning, which is a challenging problem that has long suffered from the shortage of parallel data.
1 code implementation • IJCNLP 2019 • Shen Gao, Xiuying Chen, Piji Li, Zhangming Chan, Dongyan Zhao, Rui Yan
There are two main challenges in this task: (1) the model needs to incorporate learned patterns from the prototype, but (2) should avoid copying contents other than the patternized words---such as irrelevant facts---into the generated summaries.
1 code implementation • 22 Aug 2019 • Yuting Wu, Xiao Liu, Yansong Feng, Zheng Wang, Rui Yan, Dongyan Zhao
Entity alignment is the task of linking entities with the same real-world identity from different knowledge graphs (KGs), which has been recently dominated by embedding-based methods.
Ranked #20 on Entity Alignment on DBP15k zh-en (using extra training data)
1 code implementation • IJCAI 2019 2019 • Xiuying Chen, Zhangming Chan, Shen Gao, Meng-Hsuan Yu, Dongyan Zhao, Rui Yan
Timeline summarization targets at concisely summarizing the evolution trajectory along the timeline and existing timeline summarization approaches are all based on extractive methods. In this paper, we propose the task of abstractive timeline summarization, which tends to concisely paraphrase the information in the time-stamped events. Unlike traditional document summarization, timeline summarization needs to model the time series information of the input events and summarize important events in chronological order. To tackle this challenge, we propose a memory-based timeline summarization model (MTS). Concretely, we propose a time-event memory to establish a timeline, and use the time position of events on this timeline to guide generation process. Besides, in each decoding step, we incorporate event-level information into word-level attention to avoid confusion between events. Extensive experiments are conducted on a large-scale real-world dataset, and the results show that MTS achieves the state-of-the-art performance in terms of both automatic and human evaluations.
Ranked #1 on Timeline Summarization on MTS
no code implementations • ACL 2019 • Lisong Qiu, Juntao Li, Wei Bi, Dongyan Zhao, Rui Yan
Due to its potential applications, open-domain dialogue generation has become popular and achieved remarkable progress in recent years, but sometimes suffers from generic responses.
1 code implementation • ACL 2019 • Chongyang Tao, Wei Wu, Can Xu, Wenpeng Hu, Dongyan Zhao, Rui Yan
Currently, researchers have paid great attention to retrieval-based dialogues in open-domain.
Ranked #12 on Conversational Response Selection on E-commerce
no code implementations • 18 Jun 2019 • Xiaoye Tan, Rui Yan, Chongyang Tao, Mingrui Wu
Considering that words with different characteristic in the text have different importance for classification, grouping them together separately can strengthen the semantic expression of each part.
no code implementations • ACL 2019 • Jiazhan Feng, Chongyang Tao, Wei Wu, Yansong Feng, Dongyan Zhao, Rui Yan
Under the framework, we simultaneously learn two matching models with independent training sets.
no code implementations • 11 Jun 2019 • Xueliang Zhao, Chongyang Tao, Wei Wu, Can Xu, Dongyan Zhao, Rui Yan
We present a document-grounded matching network (DGMN) for response selection that can power a knowledge-aware retrieval-based chatbot system.
1 code implementation • 31 May 2019 • Wenpeng Hu, Zhangming Chan, Bing Liu, Dongyan Zhao, Jinwen Ma, Rui Yan
Existing neural models for dialogue response generation assume that utterances are sequentially organized.
no code implementations • ICLR 2019 • Wenpeng Hu, Zhengwei Tao, Zhanxing Zhu, Bing Liu, Zhou Lin, Jinwen Ma, Dongyan Zhao, Rui Yan
A large amount of parallel data is needed to train a strong neural machine translation (NMT) system.
no code implementations • ICLR 2019 • Wenpeng Hu, Zhou Lin, Bing Liu, Chongyang Tao, Zhengwei Tao, Jinwen Ma, Dongyan Zhao, Rui Yan
Several continual learning methods have been proposed to address the problem.
1 code implementation • 23 Jan 2019 • Shen Gao, Zhaochun Ren, Yihong Eric Zhao, Dongyan Zhao, Dawei Yin, Rui Yan
In this paper, we propose the task of product-aware answer generation, which tends to generate an accurate and complete answer from large-scale unlabeled e-commerce reviews and product attributes.
Ranked #2 on Question Answering on JD Product Question Answer
no code implementations • 13 Dec 2018 • Mingyue Shang, Zhenxin Fu, Hongzhi Yin, Bo Tang, Dongyan Zhao, Rui Yan
In this paper, we incorporate the logic information with the help of the Natural Language Inference (NLI) task to the Story Cloze Test (SCT).
no code implementations • 13 Dec 2018 • Shen Gao, Xiuying Chen, Piji Li, Zhaochun Ren, Lidong Bing, Dongyan Zhao, Rui Yan
To tackle this problem, we propose the task of reader-aware abstractive summary generation, which utilizes the reader comments to help the model produce better summary about the main aspect.
Ranked #1 on Reader-Aware Summarization on RASG
no code implementations • 19 Nov 2018 • Lili Yao, Ruijian Xu, Chao Li, Dongyan Zhao, Rui Yan
To build an open-domain multi-turn conversation system is one of the most interesting and challenging tasks in Artificial Intelligence.
2 code implementations • 14 Nov 2018 • Lili Yao, Nanyun Peng, Ralph Weischedel, Kevin Knight, Dongyan Zhao, Rui Yan
Automatic storytelling is challenging since it requires generating long, coherent natural language to describes a sensible sequence of events.
1 code implementation • 14 Nov 2018 • Ning Miao, Hao Zhou, Lili Mou, Rui Yan, Lei LI
In real-world applications of natural language generation, there are often constraints on the target sentences in addition to fluency and naturalness requirements.
no code implementations • EMNLP 2018 • Juntao Li, Yan Song, Haisong Zhang, Dongmin Chen, Shuming Shi, Dongyan Zhao, Rui Yan
It is a challenging task to automatically compose poems with not only fluent expressions but also aesthetic wording.
no code implementations • EMNLP 2018 • Fangfang Zhang, Jin-Ge Yao, Rui Yan
Many modern neural document summarization systems based on encoder-decoder networks are designed to produce abstractive summaries.
1 code implementation • EMNLP 2018 • Xiuying Chen, Shen Gao, Chongyang Tao, Yan Song, Dongyan Zhao, Rui Yan
In this paper, we introduce Iterative Text Summarization (ITS), an iteration-based model for supervised extractive text summarization, inspired by the observation that it is often necessary for a human to read an article multiple times in order to fully understand and summarize its contents.
Ranked #14 on Extractive Text Summarization on CNN / Daily Mail
2 code implementations • 14 Sep 2018 • Lingfei Wu, Ian E. H. Yen, Jie Chen, Rui Yan
We thus propose the first analysis of RB from the perspective of optimization, which by interpreting RB as a Randomized Block Coordinate Descent in the infinite-dimensional space, gives a faster convergence rate compared to that of other random features.
no code implementations • 22 Aug 2018 • Chongyang Tao, Wei Wu, Can Xu, Yansong Feng, Dongyan Zhao, Rui Yan
In this paper, we study context-response matching with pre-trained contextualized representations for multi-turn response selection in retrieval-based chatbots.
no code implementations • ACL 2018 • Yanyan Jia, Yuan Ye, Yansong Feng, Yuxuan Lai, Rui Yan, Dongyan Zhao
Identifying long-span dependencies between discourse units is crucial to improve discourse parsing performance.
no code implementations • ACL 2018 • Bingfeng Luo, Yansong Feng, Zheng Wang, Songfang Huang, Rui Yan, Dongyan Zhao
The success of many natural language processing (NLP) tasks is bound by the number and quality of annotated data, but there is often a shortage of such training data.
no code implementations • 8 May 2018 • Xiaowei Tong, Zhenxin Fu, Mingyue Shang, Dongyan Zhao, Rui Yan
Automatic evaluating the performance of Open-domain dialogue system is a challenging problem.
no code implementations • ICLR 2018 • Yiping Song, Rui Yan, Cheng-Te Li, Jian-Yun Nie, Ming Zhang, Dongyan Zhao
Human-computer conversation systems have attracted much attention in Natural Language Processing.
no code implementations • ICLR 2018 • Ning Miao, Hengliang Wang, Ran Le, Chongyang Tao, Mingyue Shang, Rui Yan, Dongyan Zhao
Traditional recurrent neural network (RNN) or convolutional neural net- work (CNN) based sequence-to-sequence model can not handle tree structural data well.
no code implementations • ICLR 2018 • Wenpeng Hu, Bing Liu, Rui Yan, Dongyan Zhao, Jinwen Ma
In the paper, we propose a new question generation problem, which also requires the input of a target topic in addition to a piece of descriptive text.
no code implementations • 11 Dec 2017 • Ying Zeng, Yansong Feng, Rong Ma, Zheng Wang, Rui Yan, Chongde Shi, Dongyan Zhao
We show that this large volume of training data not only leads to a better event extractor, but also allows us to detect multiple typed events.
2 code implementations • 18 Nov 2017 • Zhenxin Fu, Xiaoye Tan, Nanyun Peng, Dongyan Zhao, Rui Yan
Results show that the proposed content preservation metric is highly correlate to human judgments, and the proposed models are able to generate sentences with higher style transfer strength and similar content preservation score comparing to auto-encoder.
Ranked #5 on Unsupervised Text Style Transfer on Yelp
no code implementations • IJCNLP 2017 • Yiping Song, Zhiliang Tian, Dongyan Zhao, Ming Zhang, Rui Yan
However, traditional seq2seq suffer from a severe weakness: during beam search decoding, they tend to rank universal replies at the top of the candidate list, resulting in the lack of diversity among candidate replies.
no code implementations • EMNLP 2017 • Lili Yao, Yaoyuan Zhang, Yansong Feng, Dongyan Zhao, Rui Yan
The study on human-computer conversation systems is a hot research topic nowadays.
no code implementations • ACL 2017 • Zhiliang Tian, Rui Yan, Lili Mou, Yiping Song, Yansong Feng, Dongyan Zhao
Generative conversational systems are attracting increasing attention in natural language processing (NLP).
no code implementations • ACL 2017 • Bingfeng Luo, Yansong Feng, Zheng Wang, Zhanxing Zhu, Songfang Huang, Rui Yan, Dongyan Zhao
We show that the dynamic transition matrix can effectively characterize the noise in the training data built by distant supervision.
no code implementations • 7 Apr 2017 • Yaoyuan Zhang, Zhenxu Ye, Yansong Feng, Dongyan Zhao, Rui Yan
For word-level studies, words are simplified but also have potential grammar errors due to different usages of words before and after simplification.
1 code implementation • 11 Jan 2017 • Chongyang Tao, Lili Mou, Dongyan Zhao, Rui Yan
Open-domain human-computer conversation has been attracting increasing attention over the past few years.
no code implementations • 14 Dec 2016 • Ruobing Xie, Zhiyuan Liu, Rui Yan, Maosong Sun
It indicates that our method could well capture the contextual information and emotion flow in dialogues, which is significant for emoji recommendation.
2 code implementations • 23 Oct 2016 • Yiping Song, Rui Yan, Xiang Li, Dongyan Zhao, Ming Zhang
In this paper, we propose a novel ensemble of retrieval-based and generation-based dialog systems in the open domain.
no code implementations • 13 Oct 2016 • Yiping Song, Lili Mou, Rui Yan, Li Yi, Zinan Zhu, Xiaohua Hu, Ming Zhang
In human-computer conversation systems, the context of a user-issued utterance is particularly important because it provides useful background information of the conversation.
no code implementations • COLING 2016 • Lili Mou, Yiping Song, Rui Yan, Ge Li, Lu Zhang, Zhi Jin
Using neural networks to generate replies in human-computer dialogue systems is attracting increasing attention over the past few years.
no code implementations • 15 Apr 2016 • Xiang Li, Lili Mou, Rui Yan, Ming Zhang
In this paper, we propose StalemateBreaker, a conversation system that can proactively introduce new content when appropriate.
no code implementations • EMNLP 2016 • Lili Mou, Zhao Meng, Rui Yan, Ge Li, Yan Xu, Lu Zhang, Zhi Jin
Transfer learning is aimed to make use of valuable knowledge in a source domain to help model performance in a target domain.
no code implementations • ACL 2016 • Lili Mou, Rui Men, Ge Li, Yan Xu, Lu Zhang, Rui Yan, Zhi Jin
In this paper, we propose the TBCNN-pair model to recognize entailment and contradiction between two sentences.
Ranked #87 on Natural Language Inference on SNLI
no code implementations • 21 Dec 2015 • Lili Mou, Rui Yan, Ge Li, Lu Zhang, Zhi Jin
Provided a specific word, we use RNNs to generate previous words and future words, either simultaneously or asynchronously, resulting in two model variants.
no code implementations • 17 Nov 2014 • Xi Peng, Jiwen Lu, Zhang Yi, Rui Yan
In this paper, we address two challenging problems in unsupervised subspace learning: 1) how to automatically identify the feature dimension of the learned subspace (i. e., automatic subspace learning), and 2) how to learn the underlying subspace in the presence of Gaussian noise (i. e., robust subspace learning).
no code implementations • 22 Sep 2014 • Xi Peng, Rui Yan, Bo Zhao, Huajin Tang, Zhang Yi
Although the methods achieve a higher recognition rate than the traditional SPM, they consume more time to encode the local descriptors extracted from the image.