Search Results for author: Hui Wan

Found 14 papers, 5 papers with code

DialDoc 2022 Shared Task: Open-Book Document-grounded Dialogue Modeling

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.

Conversational Question Answering

Evaluating Robustness of Dialogue Summarization Models in the Presence of Naturally Occurring Variations

no code implementations15 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.

How Can Context Help? Exploring Joint Retrieval of Passage and Personalized Context

no code implementations26 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.

Passage Retrieval Retrieval

Semi-Structured Object Sequence Encoders

no code implementations3 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.


Learning as Conversation: Dialogue Systems Reinforced for Information Acquisition

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.

Fast and Light-Weight Answer Text Retrieval in Dialogue Systems

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.

Re-Ranking Retrieval +1

MultiDoc2Dial: Modeling Dialogues Grounded in Multiple Documents

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.

Machine Reading Comprehension

Explaining Neural Network Predictions on Sentence Pairs via Learning Word-Group Masks

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.

Natural Language Inference Paraphrase Identification +1

doc2dial: A Goal-Oriented Document-Grounded Dialogue Dataset

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.

Quantifying and attributing time step sensitivities in present-day climate simulations conducted with EAMv1

no code implementations15 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

Multi-task Learning with Multi-head Attention for Multi-choice Reading Comprehension

no code implementations26 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.

Machine Reading Comprehension Multiple-choice +2

Rewarding Smatch: Transition-Based AMR Parsing with Reinforcement Learning

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.

AMR Parsing reinforcement-learning +1

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