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            <namePart type="given">Lucia</namePart>
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    <abstract>An overarching goal of natural language processing is to enable machines to communicate seamlessly with humans. However, natural language can be ambiguous or unclear. In cases of uncertainty, humans engage in an interactive process known as repair: asking questions and seeking clarification until their uncertainty is resolved. We propose a framework for building a visually grounded question-asking model capable of producing polar (yes-no) clarification questions to resolve misunderstandings in dialogue. Our model uses an expected information gain objective to derive informative questions from an off-the-shelf image captioner without requiring any supervised question-answer data. We demonstrate our model’s ability to pose questions that improve communicative success in a goal-oriented 20 questions game with synthetic and human answerers.</abstract>
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    <identifier type="doi">10.18653/v1/2021.emnlp-main.44</identifier>
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%0 Conference Proceedings
%T Open-domain clarification question generation without question examples
%A White, Julia
%A Poesia, Gabriel
%A Hawkins, Robert
%A Sadigh, Dorsa
%A Goodman, Noah
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F white-etal-2021-open
%X An overarching goal of natural language processing is to enable machines to communicate seamlessly with humans. However, natural language can be ambiguous or unclear. In cases of uncertainty, humans engage in an interactive process known as repair: asking questions and seeking clarification until their uncertainty is resolved. We propose a framework for building a visually grounded question-asking model capable of producing polar (yes-no) clarification questions to resolve misunderstandings in dialogue. Our model uses an expected information gain objective to derive informative questions from an off-the-shelf image captioner without requiring any supervised question-answer data. We demonstrate our model’s ability to pose questions that improve communicative success in a goal-oriented 20 questions game with synthetic and human answerers.
%R 10.18653/v1/2021.emnlp-main.44
%U https://aclanthology.org/2021.emnlp-main.44
%U https://doi.org/10.18653/v1/2021.emnlp-main.44
%P 563-570

Markdown (Informal)

[Open-domain clarification question generation without question examples](https://aclanthology.org/2021.emnlp-main.44) (White et al., EMNLP 2021)

• Open-domain clarification question generation without question examples (White et al., EMNLP 2021)

ACL

• Julia White, Gabriel Poesia, Robert Hawkins, Dorsa Sadigh, and Noah Goodman. 2021. Open-domain clarification question generation without question examples. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 563–570, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.