CUHK at SemEval-2020 Task 4: CommonSense Explanation, Reasoning and Prediction with Multi-task Learning

Abstract

This paper describes our system submitted to task 4 of SemEval 2020: Commonsense Validation and Explanation (ComVE) which consists of three sub-tasks. The challenge is to directly validate whether the system can recognize natural language statements that make sense from those that do not, and also require to generate reasonable explanation. Based on BERT architecture with multi-task setting, we propose an effective and interpretable “Explain, Reason and Predict” (ERP) system to solve the three sub-tasks about commonsense: (a) Validation, and (c) Explanation, (b) Reasoning, following the order of the competition. Inspired by cognitive studies of common sense, our system first generate a reason or understanding of the sentences and then choose which one statement makes sense, which is achieved by multi-task learning. The rational experiment validates our assumption and boost the performance. During the post-evaluation, our system has reached 92.9% accuracy in subtask A (rank 11), 89.7% accuracy in subtask B (rank 8), and BLEU score of 12.9 in subtask C (rank 9). © 2020 14th International Workshops on Semantic Evaluation, SemEval 2020 - co-located 28th International Conference on Computational Linguistics, COLING 2020, Proceedings. All rights reserved.