Search Results for author: Kevin Small

Found 14 papers, 7 papers with code

A Zero-Shot Claim Detection Framework Using Question Answering

no code implementations COLING 2022 Revanth Gangi Reddy, Sai Chetan Chinthakindi, Yi R. Fung, Kevin Small, Heng Ji

In recent years, there has been an increasing interest in claim detection as an important building block for misinformation detection.

Misinformation Object +3

Background Summarization of Event Timelines

1 code implementation24 Oct 2023 Adithya Pratapa, Kevin Small, Markus Dreyer

Generating concise summaries of news events is a challenging natural language processing task.

News Summarization Question Answering

SumREN: Summarizing Reported Speech about Events in News

1 code implementation2 Dec 2022 Revanth Gangi Reddy, Heba Elfardy, Hou Pong Chan, Kevin Small, Heng Ji

A primary objective of news articles is to establish the factual record for an event, frequently achieved by conveying both the details of the specified event (i. e., the 5 Ws; Who, What, Where, When and Why regarding the event) and how people reacted to it (i. e., reported statements).

Document Summarization Multi-Document Summarization +2

PLAtE: A Large-scale Dataset for List Page Web Extraction

no code implementations24 May 2022 Aidan San, Yuan Zhuang, Jan Bakus, Colin Lockard, David Ciemiewicz, Sandeep Atluri, Yangfeng Ji, Kevin Small, Heba Elfardy

Recently, neural models have been leveraged to significantly improve the performance of information extraction from semi-structured websites.

Attribute Attribute Extraction

Answer Consolidation: Formulation and Benchmarking

1 code implementation NAACL 2022 Wenxuan Zhou, Qiang Ning, Heba Elfardy, Kevin Small, Muhao Chen

Current question answering (QA) systems primarily consider the single-answer scenario, where each question is assumed to be paired with one correct answer.

Benchmarking Question Answering

Generating Self-Contained and Summary-Centric Question Answer Pairs via Differentiable Reward Imitation Learning

1 code implementation EMNLP 2021 Li Zhou, Kevin Small, Yong Zhang, Sandeep Atluri

Motivated by suggested question generation in conversational news recommendation systems, we propose a model for generating question-answer pairs (QA pairs) with self-contained, summary-centric questions and length-constrained, article-summarizing answers.

Imitation Learning News Recommendation +4

Inverse Reinforcement Learning with Natural Language Goals

no code implementations16 Aug 2020 Li Zhou, Kevin Small

In this paper, we propose a novel adversarial inverse reinforcement learning algorithm to learn a language-conditioned policy and reward function.

Friction Instruction Following +2

Fluent Response Generation for Conversational Question Answering

1 code implementation ACL 2020 Ashutosh Baheti, Alan Ritter, Kevin Small

In this work, we propose a method for situating QA responses within a SEQ2SEQ NLG approach to generate fluent grammatical answer responses while maintaining correctness.

Conversational Question Answering Data Augmentation +3

Active Learning in Recommendation Systems with Multi-level User Preferences

no code implementations30 Nov 2018 Yuheng Bu, Kevin Small

While recommendation systems generally observe user behavior passively, there has been an increased interest in directly querying users to learn their specific preferences.

Active Learning Recommendation Systems

End-to-End Offline Goal-Oriented Dialog Policy Learning via Policy Gradient

no code implementations7 Dec 2017 Li Zhou, Kevin Small, Oleg Rokhlenko, Charles Elkan

Learning a goal-oriented dialog policy is generally performed offline with supervised learning algorithms or online with reinforcement learning (RL).

Goal-Oriented Dialog Offline RL +1

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