QADiscourse - Discourse Relations as QA Pairs: Representation, Crowdsourcing and Baselines
| Autor: | Reut Tsarfaty, Ido Dagan, Ayal Klein, Valentina Pyatkin |
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| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
050101 languages & linguistics Computer Science - Computation and Language Interface (Java) business.industry Computer science 05 social sciences Natural language understanding 02 engineering and technology Representation (arts) computer.software_genre Crowdsourcing 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing 0501 psychology and cognitive sciences Artificial intelligence business Baseline (configuration management) Computation and Language (cs.CL) computer Natural language processing Sentence |
| Zdroj: | EMNLP (1) |
| DOI: | 10.18653/v1/2020.emnlp-main.224 |
| Popis: | 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. Comment: To appear at EMNLP 2020 |
| Databáze: | OpenAIRE |
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