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.
| Original language | English |
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| Title of host publication | EMNLP 2020 - 2020 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference |
| Publisher | Association for Computational Linguistics (ACL) |
| Pages | 2804-2819 |
| Number of pages | 16 |
| ISBN (Electronic) | 9781952148606 |
| State | Published - 2020 |
| Externally published | Yes |
| Event | 2020 Conference on Empirical Methods in Natural Language Processing, EMNLP 2020 - Virtual, Online Duration: 16 Nov 2020 → 20 Nov 2020 |
Publication series
| Name | EMNLP 2020 - 2020 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference |
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Conference
| Conference | 2020 Conference on Empirical Methods in Natural Language Processing, EMNLP 2020 |
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| City | Virtual, Online |
| Period | 16/11/20 → 20/11/20 |
Bibliographical note
Publisher Copyright:© 2020 Association for Computational Linguistics