U-Net: Machine Reading Comprehension with Unanswerable Questions

By Fu Sun, Linyang Li, Xipeng Qiu, Yang Liu. 2018

49 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Natural Language Processing

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Citations in the corpus

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019, neutral): “The results compared to prior leaderboard entries and top published work (Sun et al., 2018; Wang et al., 2018b) are shown in Table 3, excluding systems that use BERT as one of their components.”
Multi-style Generative Reading Comprehension (2019, methodology): “In order not to use words in irrelevant passages, our model introduces the concept of combined attention (Sun et al., 2018b).”