תקציר
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.
שפה מקורית | אנגלית |
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כותר פרסום המארח | EMNLP 2020 - 2020 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference |
מוציא לאור | Association for Computational Linguistics (ACL) |
עמודים | 2804-2819 |
מספר עמודים | 16 |
מסת"ב (אלקטרוני) | 9781952148606 |
סטטוס פרסום | פורסם - 2020 |
פורסם באופן חיצוני | כן |
אירוע | 2020 Conference on Empirical Methods in Natural Language Processing, EMNLP 2020 - Virtual, Online משך הזמן: 16 נוב׳ 2020 → 20 נוב׳ 2020 |
סדרות פרסומים
שם | EMNLP 2020 - 2020 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference |
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כנס
כנס | 2020 Conference on Empirical Methods in Natural Language Processing, EMNLP 2020 |
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עיר | Virtual, Online |
תקופה | 16/11/20 → 20/11/20 |
הערה ביבליוגרפית
Publisher Copyright:© 2020 Association for Computational Linguistics