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        <title>Fluent Response Generation for Conversational Question Answering</title>
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    <name type="personal">
        <namePart type="given">Ashutosh</namePart>
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        <namePart type="given">Alan</namePart>
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        <name type="personal">
            <namePart type="given">Dan</namePart>
            <namePart type="family">Jurafsky</namePart>
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    <abstract>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.</abstract>
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%0 Conference Proceedings
%T Fluent Response Generation for Conversational Question Answering
%A Baheti, Ashutosh
%A Ritter, Alan
%A Small, Kevin
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F baheti-etal-2020-fluent
%X 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.
%R 10.18653/v1/2020.acl-main.19
%U https://aclanthology.org/2020.acl-main.19
%U https://doi.org/10.18653/v1/2020.acl-main.19
%P 191-207

Markdown (Informal)

[Fluent Response Generation for Conversational Question Answering](https://aclanthology.org/2020.acl-main.19) (Baheti et al., ACL 2020)

• Fluent Response Generation for Conversational Question Answering (Baheti et al., ACL 2020)

ACL

• Ashutosh Baheti, Alan Ritter, and Kevin Small. 2020. Fluent Response Generation for Conversational Question Answering. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 191–207, Online. Association for Computational Linguistics.