CUHK at SemEval-2020 Task 4: CommonSense Explanation, Reasoning and Prediction with Multi-task Learning
This work addresses commonsense understanding in NLP, but it is incremental as it builds on existing BERT architecture with multi-task learning for a specific competition task.
The paper tackled the problem of commonsense validation, reasoning, and explanation by proposing a multi-task learning system based on BERT, achieving accuracies of 92.9% and 89.7% in validation and reasoning subtasks, and a BLEU score of 12.9 in explanation.
This paper describes our system submitted to task 4 of SemEval 2020: Commonsense Validation and Explanation (ComVE) which consists of three sub-tasks. The task is to directly validate the given sentence whether or not it makes sense and require the model to explain it. Based on BERTarchitecture with a 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, (b)Reasoning, and (c) Explanation. Inspired by cognitive studies of common sense, our system first generates a reason or understanding of the sentences and then chooses which one statement makes sense, which is achieved by multi-task learning. During the post-evaluation, our system has reached 92.9% accuracy in subtask A (rank 11), 89.7% accuracy in subtask B (rank 9), andBLEU score of 12.9 in subtask C (rank 8)