Multi-span Extractive Reading Comprehension Without Multi-span Supervision

Takumi Takahashi, Motoki Taniguchi, Tomoki Taniguchi, Tomoko Ohkuma

Abstract: This study focuses on multi-span reading comprehension (RC), which requires answering questions with multiple text spans. Existing approaches for extracting multiple answers require an elaborate dataset that contains questions requiring multiple answers. We propose a method for rewriting single-span answers extracted using several different models to detect single/multiple answer(s). With this approach, only a simple dataset and models for single-span RC are required. We consider multi-span RC with zero-shot learning. Experimental results using the DROP and QUOREF datasets demonstrate that the proposed method improves the exact match (EM) and F1 scores by a large margin on multi-span RC, compared to the baseline models. We further analyzed the effectiveness of combining different models and a strategy for such combinations when applied to multi-span RC.