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.
HFL-RC System at SemEval-2018 Task 11: Hybrid Multi-Aspects Model for Commonsense Reading Comprehension
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abstract
This paper describes the system which got the state-of-the-art results at SemEval-2018 Task 11: Machine Comprehension using Commonsense Knowledge. In this paper, we present a neural network called Hybrid Multi-Aspects (HMA) model, which mimic the human's intuitions on dealing with the multiple-choice reading comprehension. In this model, we aim to produce the predictions in multiple aspects by calculating attention among the text, question and choices, and combine these results for final predictions. Experimental results show that our HMA model could give substantial improvements over the baseline system and got the first place on the final test set leaderboard with the accuracy of 84.13%.
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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.