Adding ConceptNet relation-existence and relation-type auxiliary tasks to a BERT multiple-choice MRC model improves accuracy by about 0.7 points on SemEval-2018 Task 11 and Story Cloze Test.
Yuanfudao at SemEval-2018 Task 11: Three-way Attention and Relational Knowledge for Commonsense Machine Comprehension
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abstract
This paper describes our system for SemEval-2018 Task 11: Machine Comprehension using Commonsense Knowledge. We use Three-way Attentive Networks (TriAN) to model interactions between the passage, question and answers. To incorporate commonsense knowledge, we augment the input with relation embedding from the graph of general knowledge ConceptNet (Speer et al., 2017). As a result, our system achieves state-of-the-art performance with 83.95% accuracy on the official test data. Code is publicly available at https://github.com/intfloat/commonsense-rc
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Incorporating Relation Knowledge into Commonsense Reading Comprehension with Multi-task Learning
Adding ConceptNet relation-existence and relation-type auxiliary tasks to a BERT multiple-choice MRC model improves accuracy by about 0.7 points on SemEval-2018 Task 11 and Story Cloze Test.