Kevin Small


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.

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