no code implementations • dialdoc (ACL) 2022 • Song Feng, Siva Patel, Hui Wan
The paper presents the results of the Shared Task hosted by the Second DialDoc Workshop on Document-grounded Dialogue and Conversational Question Answering co-located at ACL 2022.
no code implementations • 15 Nov 2023 • Ankita Gupta, Chulaka Gunasekara, Hui Wan, Jatin Ganhotra, Sachindra Joshi, Marina Danilevsky
We find that both fine-tuned and instruction-tuned models are affected by input variations, with the latter being more susceptible, particularly to dialogue-level perturbations.
no code implementations • 26 Aug 2023 • Hui Wan, Hongkang Li, Songtao Lu, Xiaodong Cui, Marina Danilevsky
The integration of external personalized context information into document-grounded conversational systems has significant potential business value, but has not been well-studied.
no code implementations • 3 Jan 2023 • Rudra Murthy V, Riyaz Bhat, Chulaka Gunasekara, Siva Sankalp Patel, Hui Wan, Tejas Indulal Dhamecha, Danish Contractor, Marina Danilevsky
In this paper we explore the task of modeling semi-structured object sequences; in particular, we focus our attention on the problem of developing a structure-aware input representation for such sequences.
1 code implementation • NAACL 2022 • Pengshan Cai, Hui Wan, Fei Liu, Mo Yu, Hong Yu, Sachindra Joshi
We propose novel AI-empowered chat bots for learning as conversation where a user does not read a passage but gains information and knowledge through conversation with a teacher bot.
1 code implementation • NAACL (ACL) 2022 • Hui Wan, Siva Sankalp Patel, J. William Murdock, Saloni Potdar, Sachindra Joshi
Dialogue systems can benefit from being able to search through a corpus of text to find information relevant to user requests, especially when encountering a request for which no manually curated response is available.
1 code implementation • EMNLP 2021 • Song Feng, Siva Sankalp Patel, Hui Wan, Sachindra Joshi
We propose MultiDoc2Dial, a new task and dataset on modeling goal-oriented dialogues grounded in multiple documents.
no code implementations • NAACL 2021 • Hui Wan, Song Feng, Chulaka Gunasekara, Siva Sankalp Patel, Sachindra Joshi, Luis Lastras
Machine reading comprehension is a challenging task especially for querying documents with deep and interconnected contexts.
1 code implementation • NAACL 2021 • Hanjie Chen, Song Feng, Jatin Ganhotra, Hui Wan, Chulaka Gunasekara, Sachindra Joshi, Yangfeng Ji
Most existing methods generate post-hoc explanations for neural network models by identifying individual feature attributions or detecting interactions between adjacent features.
2 code implementations • EMNLP 2020 • Song Feng, Hui Wan, Chulaka Gunasekara, Siva Sankalp Patel, Sachindra Joshi, Luis A. Lastras
We introduce doc2dial, a new dataset of goal-oriented dialogues that are grounded in the associated documents.
no code implementations • 15 Oct 2020 • Hui Wan, Shixuan Zhang, Philip J. Rasch, Vincent E. Larson, Xubin Zeng, Huiping Yan
This study assesses the relative importance of time integration error in present-day climate simulations conducted with the atmosphere component of the Energy Exascale Earth System Model version 1 (EAMv1) at 1-degree horizontal resolution.
Atmospheric and Oceanic Physics
no code implementations • 26 Feb 2020 • Hui Wan
Multiple-choice Machine Reading Comprehension (MRC) is an important and challenging Natural Language Understanding (NLU) task, in which a machine must choose the answer to a question from a set of choices, with the question placed in context of text passages or dialog.
no code implementations • ACL 2019 • Tahira Naseem, Abhishek Shah, Hui Wan, Radu Florian, Salim Roukos, Miguel Ballesteros
Our work involves enriching the Stack-LSTM transition-based AMR parser (Ballesteros and Al-Onaizan, 2017) by augmenting training with Policy Learning and rewarding the Smatch score of sampled graphs.
Ranked #24 on AMR Parsing on LDC2017T10
no code implementations • CONLL 2018 • Hui Wan, Tahira Naseem, Young-suk Lee, Vittorio Castelli, Miguel Ballesteros
This paper presents the IBM Research AI submission to the CoNLL 2018 Shared Task on Parsing Universal Dependencies.