Publication | Open Access
A Co-Matching Model for Multi-choice Reading Comprehension
108
Citations
19
References
2018
Year
Unknown Venue
EngineeringEducationTextual EntailmentCorpus LinguisticsText MiningNatural Language ProcessingInformation RetrievalReading ComprehensionComputational LinguisticsVisual Question AnsweringMulti-choice Reading ComprehensionRace Dataset DemonstrateMachine TranslationQuestion AnsweringNlp TaskLanguage TechnologyNew Co-matching ApproachRetrieval Augmented GenerationCo-matching ModelLanguage ComprehensionReading Comprehension StrategiesLinguistics
Multi-choice reading comprehension is a challenging task, which involves the matching between a passage and a question-answer pair. This paper proposes a new co-matching approach to this problem, which jointly models whether a passage can match both a question and a candidate answer. Experimental results on the RACE dataset demonstrate that our approach achieves state-of-the-art performance.
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