no code implementations • ACL 2022 • Runxin Sun, Shizhu He, Chong Zhu, Yaohan He, Jinlong Li, Jun Zhao, Kang Liu
Text-to-SQL aims to parse natural language questions into SQL queries, which is valuable in providing an easy interface to access large databases.
no code implementations • COLING 2022 • Ran Song, Shizhu He, Suncong Zheng, Shengxiang Gao, Kang Liu, Zhengtao Yu, Jun Zhao
In fact, the semantics of a relation can be expressed by three kinds of graphs: factual graph, ontology graph, textual description graph, and they can complement each other.
no code implementations • TU (COLING) 2022 • Minjun Zhu, Yixuan Weng, Bin Li, Shizhu He, Kang Liu, Jun Zhao
In this work, we propose a knowledge transfer method with visual prompt (VPTG) fusing multi-modal data, which is a flexible module that can utilize the text-only seq2seq model to handle visual dialogue tasks.
1 code implementation • EMNLP 2021 • Qingbin Liu, Pengfei Cao, Cao Liu, Jiansong Chen, Xunliang Cai, Fan Yang, Shizhu He, Kang Liu, Jun Zhao
This paradigm is often impractical in real-world applications since online dialogue systems usually involve continually emerging new data and domains.
1 code implementation • SemEval (NAACL) 2022 • Fei Xia, Bin Li, Yixuan Weng, Shizhu He, Bin Sun, Shutao Li, Kang Liu, Jun Zhao
For the classification sub-task, we adopt the DeBERTa-v3 pre-trained model for fine-tuning datasets of different languages.
2 code implementations • SemEval (NAACL) 2022 • Bin Li, Yixuan Weng, Fei Xia, Shizhu He, Bin Sun, Shutao Li
This paper introduces the approach of Team LingJing’s experiments on SemEval-2022 Task 1 Comparing Dictionaries and Word Embeddings (CODWOE).
no code implementations • Findings (ACL) 2022 • Guirong Bai, Shizhu He, Kang Liu, Jun Zhao
We first formulate incremental learning for medical intent detection.
1 code implementation • 23 Apr 2024 • Yao Xu, Shizhu He, Jiabei Chen, ZiHao Wang, Yangqiu Song, Hanghang Tong, Kang Liu, Jun Zhao
To simulate real-world scenarios and evaluate the ability of LLMs to integrate internal and external knowledge, in this paper, we propose leveraging LLMs for QA under Incomplete Knowledge Graph (IKGQA), where the given KG doesn't include all the factual triples involved in each question.
no code implementations • 28 Mar 2024 • Yuhong He, Yongqi Zhang, Shizhu He, Jun Wan
This approach eliminates the need for entity annotation and increases the transparency of the MDG process by explicitly generating the intermediate reasoning chain.
1 code implementation • 22 Mar 2024 • Huanxuan Liao, Shizhu He, Yao Xu, Yuanzhe Zhang, Kang Liu, Shengping Liu, Jun Zhao
Retrieval-Augmented-Generation and Gener-ation-Augmented-Generation have been proposed to enhance the knowledge required for question answering over Large Language Models (LLMs).
no code implementations • 9 Mar 2024 • Wangtao Sun, Haotian Xu, Xuanqing Yu, Pei Chen, Shizhu He, Jun Zhao, Kang Liu
Although Large Language Models (LLMs) are showing impressive performance on a wide range of Natural Language Processing tasks, researchers have found that they still have limited ability to conduct induction.
no code implementations • 8 Mar 2024 • Wangtao Sun, Shizhu He, Jun Zhao, Kang Liu
With good explanatory power and controllability, rule-based methods play an important role in many tasks such as knowledge reasoning and decision support.
1 code implementation • 20 Feb 2024 • Tongxu Luo, Jiahe Lei, Fangyu Lei, Weihao Liu, Shizhu He, Jun Zhao, Kang Liu
Fine-tuning is often necessary to enhance the adaptability of Large Language Models (LLM) to downstream tasks.
1 code implementation • 15 Feb 2024 • Yixuan Weng, Shizhu He, Kang Liu, Shengping Liu, Jun Zhao
This heightens the need to control model behaviors.
1 code implementation • 13 Nov 2023 • Wangtao Sun, Xuanqing Yu, Shizhu He, Jun Zhao, Kang Liu
Black-box Large Language Models (LLMs) have shown great power in solving various tasks and are considered general problem solvers.
1 code implementation • 29 Oct 2023 • Qianren Mao, Shaobo Zhao, Jiarui Li, Xiaolei Gu, Shizhu He, Bo Li, JianXin Li
Pre-trained sentence representations are crucial for identifying significant sentences in unsupervised document extractive summarization.
2 code implementations • 23 Oct 2023 • Fangyu Lei, Qian Liu, Yiming Huang, Shizhu He, Jun Zhao, Kang Liu
The rapid development of Large Language Models (LLMs) has led to great strides in model capabilities like long-context understanding and reasoning.
no code implementations • 23 Oct 2023 • Fangyu Lei, Tongxu Luo, Pengqi Yang, Weihao Liu, Hanwen Liu, Jiahe Lei, Yiming Huang, Yifan Wei, Shizhu He, Jun Zhao, Kang Liu
Table-based question answering (TableQA) is an important task in natural language processing, which requires comprehending tables and employing various reasoning ways to answer the questions.
no code implementations • 23 Oct 2023 • Zhiyuan Fan, Shizhu He
Open Information Extraction (OpenIE) is a fundamental yet challenging task in Natural Language Processing, which involves extracting all triples (subject, predicate, object) from a given sentence.
1 code implementation • 17 Oct 2023 • Yao Xu, Shizhu He, Cunguang Wang, Li Cai, Kang Liu, Jun Zhao
However, these methods train KG embeddings and neural set operators concurrently on both simple (one-hop) and complex (multi-hop and logical) queries, which causes performance degradation on simple queries and low training efficiency.
no code implementations • 9 Sep 2023 • Weihao Liu, Fangyu Lei, Tongxu Luo, Jiahe Lei, Shizhu He, Jun Zhao, Kang Liu
Most importantly, we propose a Type-specific In-context Learning Strategy for MMHQA, enabling LLMs to leverage their powerful performance in this task.
1 code implementation • 20 Aug 2023 • Yixuan Weng, Zhiqi Wang, Huanxuan Liao, Shizhu He, Shengping Liu, Kang Liu, Jun Zhao
With the burgeoning development in the realm of large language models (LLMs), the demand for efficient incremental training tailored to specific industries and domains continues to increase.
no code implementations • 23 May 2023 • Minjun Zhu, Yixuan Weng, Shizhu He, Kang Liu, Jun Zhao
In Textual question answering (TQA) systems, complex questions often require retrieving multiple textual fact chains with multiple reasoning steps.
1 code implementation • 19 May 2023 • Fangyu Lei, Xiang Li, Yifan Wei, Shizhu He, Yiming Huang, Jun Zhao, Kang Liu
In this paper, we propose a three-stage TextTableQA framework S3HQA, which comprises of retriever, selector, and reasoner.
1 code implementation • 9 May 2023 • Yixuan Weng, Bin Li, Fei Xia, Minjun Zhu, Bin Sun, Shizhu He, Kang Liu, Jun Zhao
The medical conversational question answering (CQA) system aims at providing a series of professional medical services to improve the efficiency of medical care.
3 code implementations • 4 Apr 2023 • Yixuan Weng, Minjun Zhu, Fei Xia, Bin Li, Shizhu He, Kang Liu, Jun Zhao
Our work highlights the potential of seamlessly unifying explicit rule learning via CoNNs and implicit pattern learning in LMs, paving the way for true symbolic comprehension capabilities.
no code implementations • 7 Jan 2023 • Yinyu Lan, Shizhu He, Kang Liu, Jun Zhao
The former has high accuracy and good interpretability, but a major challenge is to obtain effective rules on large-scale KGs.
1 code implementation • 19 Dec 2022 • Yixuan Weng, Minjun Zhu, Fei Xia, Bin Li, Shizhu He, Shengping Liu, Bin Sun, Kang Liu, Jun Zhao
By performing a backward verification of the answers that LLM deduced for itself, we can obtain interpretable answer validation scores to select the candidate answer with the highest score.
no code implementations • 17 Oct 2022 • Minjun Zhu, Yixuan Weng, Shizhu He, Kang Liu, Jun Zhao
Recently, natural language database (NLDB) conducts complex QA in knowledge base with textual evidences rather than structured representations, this task attracts a lot of attention because of the flexibility and richness of textual evidence.
1 code implementation • COLING 2022 • Fangyu Lei, Shizhu He, Xiang Li, Jun Zhao, Kang Liu
In the real-world question answering scenarios, hybrid form combining both tabular and textual contents has attracted more and more attention, among which numerical reasoning problem is one of the most typical and challenging problems.
1 code implementation • 20 Apr 2022 • Fei Xia, Bin Li, Yixuan Weng, Shizhu He, Kang Liu, Bin Sun, Shutao Li, Jun Zhao
The medical conversational system can relieve the burden of doctors and improve the efficiency of healthcare, especially during the pandemic.
1 code implementation • 8 Dec 2021 • Yixuan Weng, Fei Xia, Bin Li, Xiusheng Huang, Shizhu He
To address the above issue, this paper proposes an new method for acronym disambiguation, named as ADBCMM, which can significantly improve the performance of low-resource languages by building counterfactuals and multilingual mixing.
no code implementations • 10 Aug 2021 • Qingbin Liu, Xiaoyan Yu, Shizhu He, Kang Liu, Jun Zhao
In this paper, we propose Lifelong Intent Detection (LID), which continually trains an ID model on new data to learn newly emerging intents while avoiding catastrophically forgetting old data.
no code implementations • 27 May 2021 • Yinyu Lan, Shizhu He, Xiangrong Zeng, Shengping Liu, Kang Liu, Jun Zhao
To address the above issues, this paper proposes two novel path-based reasoning methods to solve the sparsity issues of entity and path respectively, which adopts the textual semantic information of entities and paths for MedKGC.
no code implementations • COLING 2020 • Guirong Bai, Shizhu He, Kang Liu, Jun Zhao, Zaiqing Nie
Active learning is able to significantly reduce the annotation cost for data-driven techniques.
no code implementations • IJCNLP 2019 • Xiangrong Zeng, Shizhu He, Daojian Zeng, Kang Liu, Shengping Liu, Jun Zhao
Existing works didn{'}t consider the extraction order of relational facts in a sentence.
no code implementations • CONLL 2019 • Cao Liu, Kang Liu, Shizhu He, Zaiqing Nie, Jun Zhao
Facing this challenge, we present a response generation model which incorporates Interlocutor-aware Contexts into Recurrent Encoder-Decoder frameworks (ICRED) for RGMPC.
no code implementations • IJCNLP 2019 • Cao Liu, Kang Liu, Shizhu He, Zaiqing Nie, Jun Zhao
Meanwhile, such generated question can express the given predicate and correspond to a definitive answer.
no code implementations • 21 Aug 2019 • Qingbin Liu, Shizhu He, Kang Liu, Shengping Liu, Jun Zhao
How to integrate the semantic information of pre-defined ontology and dialogue text (heterogeneous texts) to generate unknown values and improve performance becomes a severe challenge.
no code implementations • ACL 2019 • Cao Liu, Shizhu He, Kang Liu, Jun Zhao
To tackle the above two problems, we present a Vocabulary Pyramid Network (VPN) which is able to incorporate multi-pass encoding and decoding with multi-level vocabularies into response generation.
no code implementations • ACL 2019 • Xiang Zhang, Shizhu He, Kang Liu, Jun Zhao
To keep the model aware of the underlying grammar in target sequences, many constrained decoders were devised in a multi-stage paradigm, which decode to the sketches or abstract syntax trees first, and then decode to target semantic tokens.
no code implementations • COLING 2018 • Yanchao Hao, Hao liu, Shizhu He, Kang Liu, Jun Zhao
Question Answering over Knowledge Bases (KB-QA), which automatically answer natural language questions based on the facts contained by a knowledge base, is one of the most important natural language processing (NLP) tasks.
1 code implementation • ACL 2018 • Xiangrong Zeng, Daojian Zeng, Shizhu He, Kang Liu, Jun Zhao
The relational facts in sentences are often complicated.
Ranked #12 on Relation Extraction on NYT11-HRL
no code implementations • IJCNLP 2017 • Shangmin Guo, Kang Liu, Shizhu He, Cao Liu, Jun Zhao, Zhuoyu Wei
The IJCNLP-2017 Multi-choice Question Answering(MCQA) task aims at exploring the performance of current Question Answering(QA) techniques via the realworld complex questions collected from Chinese Senior High School Entrance Examination papers and CK12 website1.
no code implementations • ACL 2017 • Shizhu He, Cao Liu, Kang Liu, Jun Zhao
Generating answer with natural language sentence is very important in real-world question answering systems, which needs to obtain a right answer as well as a coherent natural response.
no code implementations • ACL 2017 • Yanchao Hao, Yuanzhe Zhang, Kang Liu, Shizhu He, Zhanyi Liu, Hua Wu, Jun Zhao
This simple representation strategy is not easy to express the proper information in the question.
1 code implementation • EACL 2017 • Shangmin Guo, Xiangrong Zeng, Shizhu He, Kang Liu, Jun Zhao
As one of the most important test of China, Gaokao is designed to be difficult enough to distinguish the excellent high school students.
no code implementations • 3 Jun 2016 • Yuanzhe Zhang, Kang Liu, Shizhu He, Guoliang Ji, Zhanyi Liu, Hua Wu, Jun Zhao
With the rapid growth of knowledge bases (KBs) on the web, how to take full advantage of them becomes increasingly important.