Kevin Small


2022

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Answer Consolidation: Formulation and Benchmarking
Wenxuan Zhou | Qiang Ning | Heba Elfardy | Kevin Small | Muhao Chen
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Current question answering (QA) systems primarily consider the single-answer scenario, where each question is assumed to be paired with one correct answer. However, in many real-world QA applications, multiple answer scenarios arise where consolidating answers into a comprehensive and non-redundant set of answers is a more efficient user interface. In this paper, we formulate the problem of answer consolidation, where answers are partitioned into multiple groups, each representing different aspects of the answer set. Then, given this partitioning, a comprehensive and non-redundant set of answers can be constructed by picking one answer from each group. To initiate research on answer consolidation, we construct a dataset consisting of 4,699 questions and 24,006 sentences and evaluate multiple models. Despite a promising performance achieved by the best-performing supervised models, we still believe this task has room for further improvements.

2021

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Summary-Oriented Question Generation for Informational Queries
Xusen Yin | Li Zhou | Kevin Small | Jonathan May
Proceedings of the 1st Workshop on Document-grounded Dialogue and Conversational Question Answering (DialDoc 2021)

Users frequently ask simple factoid questions for question answering (QA) systems, attenuating the impact of myriad recent works that support more complex questions. Prompting users with automatically generated suggested questions (SQs) can improve user understanding of QA system capabilities and thus facilitate more effective use. We aim to produce self-explanatory questions that focus on main document topics and are answerable with variable length passages as appropriate. We satisfy these requirements by using a BERT-based Pointer-Generator Network trained on the Natural Questions (NQ) dataset. Our model shows SOTA performance of SQ generation on the NQ dataset (20.1 BLEU-4). We further apply our model on out-of-domain news articles, evaluating with a QA system due to the lack of gold questions and demonstrate that our model produces better SQs for news articles – with further confirmation via a human evaluation.

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Generating Self-Contained and Summary-Centric Question Answer Pairs via Differentiable Reward Imitation Learning
Li Zhou | Kevin Small | Yong Zhang | Sandeep Atluri
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

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. We begin by collecting a new dataset of news articles with questions as titles and pairing them with summaries of varying length. This dataset is used to learn a QA pair generation model producing summaries as answers that balance brevity with sufficiency jointly with their corresponding questions. We then reinforce the QA pair generation process with a differentiable reward function to mitigate exposure bias, a common problem in natural language generation. Both automatic metrics and human evaluation demonstrate these QA pairs successfully capture the central gists of the articles and achieve high answer accuracy.

2020

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Fluent Response Generation for Conversational Question Answering
Ashutosh Baheti | Alan Ritter | Kevin Small
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Question answering (QA) is an important aspect of open-domain conversational agents, garnering specific research focus in the conversational QA (ConvQA) subtask. One notable limitation of recent ConvQA efforts is the response being answer span extraction from the target corpus, thus ignoring the natural language generation (NLG) aspect of high-quality conversational agents. 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. From a technical perspective, we use data augmentation to generate training data for an end-to-end system. Specifically, we develop Syntactic Transformations (STs) to produce question-specific candidate answer responses and rank them using a BERT-based classifier (Devlin et al., 2019). Human evaluation on SQuAD 2.0 data (Rajpurkar et al., 2018) demonstrate that the proposed model outperforms baseline CoQA and QuAC models in generating conversational responses. We further show our model’s scalability by conducting tests on the CoQA dataset. The code and data are available at https://github.com/abaheti95/QADialogSystem.

2010

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Proceedings of the NAACL HLT 2010 Workshop on Active Learning for Natural Language Processing
Burr Settles | Kevin Small | Katrin Tomanek
Proceedings of the NAACL HLT 2010 Workshop on Active Learning for Natural Language Processing

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Object Search: Supporting Structured Queries in Web Search Engines
Kim Pham | Nicholas Rizzolo | Kevin Small | Kevin Chen-Chuan Chang | Dan Roth
Proceedings of the NAACL HLT 2010 Workshop on Semantic Search

2009

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Interactive Feature Space Construction using Semantic Information
Dan Roth | Kevin Small
Proceedings of the Thirteenth Conference on Computational Natural Language Learning (CoNLL-2009)

2007

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All links are not the same: evaluating word alignments for statistical machine translation
Paul C. Davis | Zhuli Xie | Kevin Small
Proceedings of Machine Translation Summit XI: Papers