@inproceedings{pyatkin-etal-2020-qadiscourse,
title = "{QAD}iscourse - {D}iscourse {R}elations as {QA} {P}airs: {R}epresentation, {C}rowdsourcing and {B}aselines",
author = "Pyatkin, Valentina and
Klein, Ayal and
Tsarfaty, Reut and
Dagan, Ido",
editor = "Webber, Bonnie and
Cohn, Trevor and
He, Yulan and
Liu, Yang",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.emnlp-main.224",
doi = "10.18653/v1/2020.emnlp-main.224",
pages = "2804--2819",
abstract = "Discourse relations describe how two propositions relate to one another, and identifying them automatically is an integral part of natural language understanding. However, annotating discourse relations typically requires expert annotators. Recently, different semantic aspects of a sentence have been represented and crowd-sourced via question-and-answer (QA) pairs. This paper proposes a novel representation of discourse relations as QA pairs, which in turn allows us to crowd-source wide-coverage data annotated with discourse relations, via an intuitively appealing interface for composing such questions and answers. Based on our proposed representation, we collect a novel and wide-coverage QADiscourse dataset, and present baseline algorithms for predicting QADiscourse relations.",
}
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<abstract>Discourse relations describe how two propositions relate to one another, and identifying them automatically is an integral part of natural language understanding. However, annotating discourse relations typically requires expert annotators. Recently, different semantic aspects of a sentence have been represented and crowd-sourced via question-and-answer (QA) pairs. This paper proposes a novel representation of discourse relations as QA pairs, which in turn allows us to crowd-source wide-coverage data annotated with discourse relations, via an intuitively appealing interface for composing such questions and answers. Based on our proposed representation, we collect a novel and wide-coverage QADiscourse dataset, and present baseline algorithms for predicting QADiscourse relations.</abstract>
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%0 Conference Proceedings
%T QADiscourse - Discourse Relations as QA Pairs: Representation, Crowdsourcing and Baselines
%A Pyatkin, Valentina
%A Klein, Ayal
%A Tsarfaty, Reut
%A Dagan, Ido
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F pyatkin-etal-2020-qadiscourse
%X Discourse relations describe how two propositions relate to one another, and identifying them automatically is an integral part of natural language understanding. However, annotating discourse relations typically requires expert annotators. Recently, different semantic aspects of a sentence have been represented and crowd-sourced via question-and-answer (QA) pairs. This paper proposes a novel representation of discourse relations as QA pairs, which in turn allows us to crowd-source wide-coverage data annotated with discourse relations, via an intuitively appealing interface for composing such questions and answers. Based on our proposed representation, we collect a novel and wide-coverage QADiscourse dataset, and present baseline algorithms for predicting QADiscourse relations.
%R 10.18653/v1/2020.emnlp-main.224
%U https://aclanthology.org/2020.emnlp-main.224
%U https://doi.org/10.18653/v1/2020.emnlp-main.224
%P 2804-2819
Markdown (Informal)
[QADiscourse - Discourse Relations as QA Pairs: Representation, Crowdsourcing and Baselines](https://aclanthology.org/2020.emnlp-main.224) (Pyatkin et al., EMNLP 2020)
ACL