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.
|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)|
|Number of pages||16|
|State||Published - 2020|
|Event||2020 Conference on Empirical Methods in Natural Language Processing, EMNLP 2020 - Virtual, Online|
Duration: 16 Nov 2020 → 20 Nov 2020
|Name||EMNLP 2020 - 2020 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference|
|Conference||2020 Conference on Empirical Methods in Natural Language Processing, EMNLP 2020|
|Period||16/11/20 → 20/11/20|
Bibliographical noteFunding Information:
We would like to thank Amit Moryossef for his help with the implementation of the frontend, and Julian Michael, Gabriel Stanovsky and the anonymous reviewers for their feedback and suggestions. This work was supported in part by grants from Intel Labs, Facebook, the Israel Science Foundation grant 1951/17 and the German Research Foundation through the German-Israeli Project Cooperation (DIP, grant DA 1600/1-1) and by an ERC-StG grant #677352 and an ISF grant #1739/26.
© 2020 Association for Computational Linguistics