U-Net: Machine Reading Comprehension with Unanswerable Questions
This addresses the challenge of handling unanswerable questions in reading comprehension for natural language processing, representing an incremental improvement over pipeline models.
The paper tackles the problem of machine reading comprehension with unanswerable questions by proposing U-Net, a unified model with answer and no-answer pointers and an answer verifier, achieving an F1 score of 71.7 on the SQuAD 2.0 dataset.
Machine reading comprehension with unanswerable questions is a new challenging task for natural language processing. A key subtask is to reliably predict whether the question is unanswerable. In this paper, we propose a unified model, called U-Net, with three important components: answer pointer, no-answer pointer, and answer verifier. We introduce a universal node and thus process the question and its context passage as a single contiguous sequence of tokens. The universal node encodes the fused information from both the question and passage, and plays an important role to predict whether the question is answerable and also greatly improves the conciseness of the U-Net. Different from the state-of-art pipeline models, U-Net can be learned in an end-to-end fashion. The experimental results on the SQuAD 2.0 dataset show that U-Net can effectively predict the unanswerability of questions and achieves an F1 score of 71.7 on SQuAD 2.0.