@inproceedings{wojatzki-etal-2018-agree,
title = "Agree or Disagree: Predicting Judgments on Nuanced Assertions",
author = "Wojatzki, Michael and
Zesch, Torsten and
Mohammad, Saif and
Kiritchenko, Svetlana",
editor = "Nissim, Malvina and
Berant, Jonathan and
Lenci, Alessandro",
booktitle = "Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics",
month = jun,
year = "2018",
address = "New Orleans, Louisiana",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S18-2026",
doi = "10.18653/v1/S18-2026",
pages = "214--224",
abstract = "Being able to predict whether people agree or disagree with an assertion (i.e. an explicit, self-contained statement) has several applications ranging from predicting how many people will like or dislike a social media post to classifying posts based on whether they are in accordance with a particular point of view. We formalize this as two NLP tasks: predicting judgments of (i) individuals and (ii) groups based on the text of the assertion and previous judgments. We evaluate a wide range of approaches on a crowdsourced data set containing over 100,000 judgments on over 2,000 assertions. We find that predicting individual judgments is a hard task with our best results only slightly exceeding a majority baseline, but that judgments of groups can be more reliably predicted using a Siamese neural network, which outperforms all other approaches by a wide margin.",
}
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<abstract>Being able to predict whether people agree or disagree with an assertion (i.e. an explicit, self-contained statement) has several applications ranging from predicting how many people will like or dislike a social media post to classifying posts based on whether they are in accordance with a particular point of view. We formalize this as two NLP tasks: predicting judgments of (i) individuals and (ii) groups based on the text of the assertion and previous judgments. We evaluate a wide range of approaches on a crowdsourced data set containing over 100,000 judgments on over 2,000 assertions. We find that predicting individual judgments is a hard task with our best results only slightly exceeding a majority baseline, but that judgments of groups can be more reliably predicted using a Siamese neural network, which outperforms all other approaches by a wide margin.</abstract>
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%0 Conference Proceedings
%T Agree or Disagree: Predicting Judgments on Nuanced Assertions
%A Wojatzki, Michael
%A Zesch, Torsten
%A Mohammad, Saif
%A Kiritchenko, Svetlana
%Y Nissim, Malvina
%Y Berant, Jonathan
%Y Lenci, Alessandro
%S Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F wojatzki-etal-2018-agree
%X Being able to predict whether people agree or disagree with an assertion (i.e. an explicit, self-contained statement) has several applications ranging from predicting how many people will like or dislike a social media post to classifying posts based on whether they are in accordance with a particular point of view. We formalize this as two NLP tasks: predicting judgments of (i) individuals and (ii) groups based on the text of the assertion and previous judgments. We evaluate a wide range of approaches on a crowdsourced data set containing over 100,000 judgments on over 2,000 assertions. We find that predicting individual judgments is a hard task with our best results only slightly exceeding a majority baseline, but that judgments of groups can be more reliably predicted using a Siamese neural network, which outperforms all other approaches by a wide margin.
%R 10.18653/v1/S18-2026
%U https://aclanthology.org/S18-2026
%U https://doi.org/10.18653/v1/S18-2026
%P 214-224
Markdown (Informal)
[Agree or Disagree: Predicting Judgments on Nuanced Assertions](https://aclanthology.org/S18-2026) (Wojatzki et al., *SEM 2018)
ACL
- Michael Wojatzki, Torsten Zesch, Saif Mohammad, and Svetlana Kiritchenko. 2018. Agree or Disagree: Predicting Judgments on Nuanced Assertions. In Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics, pages 214–224, New Orleans, Louisiana. Association for Computational Linguistics.