@inproceedings{mazuecos-etal-2021-impact,
title = "The Impact of Answers in Referential Visual Dialog",
author = "Mazuecos, Mauricio and
Blackburn, Patrick and
Benotti, Luciana",
editor = "Howes, Christine and
Dobnik, Simon and
Breitholtz, Ellen and
Chatzikyriakidis, Stergios",
booktitle = "Proceedings of the Reasoning and Interaction Conference (ReInAct 2021)",
month = oct,
year = "2021",
address = "Gothenburg, Sweden",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.reinact-1.2",
pages = "8--13",
abstract = "In the visual dialog task GuessWhat?! two players maintain a dialog in order to identify a secret object in an image. Computationally, this is modeled using a question generation module and a guesser module for the questioner role and an answering model, the Oracle, to answer the generated questions. This raises a question: what{'}s the risk of having an imperfect oracle model?. Here we present work in progress in the study of the impact of different answering models in human generated questions in GuessWhat?!. We show that having access to better quality answers has a direct impact on the guessing task for human dialog and argue that better answers could help train better question generation models.",
}
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<abstract>In the visual dialog task GuessWhat?! two players maintain a dialog in order to identify a secret object in an image. Computationally, this is modeled using a question generation module and a guesser module for the questioner role and an answering model, the Oracle, to answer the generated questions. This raises a question: what’s the risk of having an imperfect oracle model?. Here we present work in progress in the study of the impact of different answering models in human generated questions in GuessWhat?!. We show that having access to better quality answers has a direct impact on the guessing task for human dialog and argue that better answers could help train better question generation models.</abstract>
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%0 Conference Proceedings
%T The Impact of Answers in Referential Visual Dialog
%A Mazuecos, Mauricio
%A Blackburn, Patrick
%A Benotti, Luciana
%Y Howes, Christine
%Y Dobnik, Simon
%Y Breitholtz, Ellen
%Y Chatzikyriakidis, Stergios
%S Proceedings of the Reasoning and Interaction Conference (ReInAct 2021)
%D 2021
%8 October
%I Association for Computational Linguistics
%C Gothenburg, Sweden
%F mazuecos-etal-2021-impact
%X In the visual dialog task GuessWhat?! two players maintain a dialog in order to identify a secret object in an image. Computationally, this is modeled using a question generation module and a guesser module for the questioner role and an answering model, the Oracle, to answer the generated questions. This raises a question: what’s the risk of having an imperfect oracle model?. Here we present work in progress in the study of the impact of different answering models in human generated questions in GuessWhat?!. We show that having access to better quality answers has a direct impact on the guessing task for human dialog and argue that better answers could help train better question generation models.
%U https://aclanthology.org/2021.reinact-1.2
%P 8-13
Markdown (Informal)
[The Impact of Answers in Referential Visual Dialog](https://aclanthology.org/2021.reinact-1.2) (Mazuecos et al., ReInAct 2021)
ACL
- Mauricio Mazuecos, Patrick Blackburn, and Luciana Benotti. 2021. The Impact of Answers in Referential Visual Dialog. In Proceedings of the Reasoning and Interaction Conference (ReInAct 2021), pages 8–13, Gothenburg, Sweden. Association for Computational Linguistics.