Dependency Parsing as MRC-based Span-Span Prediction

• URL: http://arxiv.org/abs/2105.07654v1
• Date: Mon, 17 May 2021 08:03:48 GMT
• Title: Dependency Parsing as MRC-based Span-Span Prediction
• Authors: Leilei Gan, Yuxian Meng, Kun Kuang, Xiaofei Sun, Chun Fan, Fei Wu and Jiwei Li
• Abstract summary: Higher-order methods for dependency parsing can partially but not fully address the issue that edges in dependency tree should be constructed at the text span/subtree level rather than word level. We propose a new method for dependency parsing to address this issue.
• Score: 29.956515394820673
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Higher-order methods for dependency parsing can partially but not fully addresses the issue that edges in dependency tree should be constructed at the text span/subtree level rather than word level. % This shortcoming can cause an incorrect span covered the corresponding tree rooted at a certain word though the word is correctly linked to its head. In this paper, we propose a new method for dependency parsing to address this issue. The proposed method constructs dependency trees by directly modeling span-span (in other words, subtree-subtree) relations. It consists of two modules: the {\it text span proposal module} which proposes candidate text spans, each of which represents a subtree in the dependency tree denoted by (root, start, end); and the {\it span linking module}, which constructs links between proposed spans. We use the machine reading comprehension (MRC) framework as the backbone to formalize the span linking module in an MRC setup, where one span is used as a query to extract the text span/subtree it should be linked to. The proposed method comes with the following merits: (1) it addresses the fundamental problem that edges in a dependency tree should be constructed between subtrees; (2) the MRC framework allows the method to retrieve missing spans in the span proposal stage, which leads to higher recall for eligible spans. Extensive experiments on the PTB, CTB and Universal Dependencies (UD) benchmarks demonstrate the effectiveness of the proposed method. We are able to achieve new SOTA performances on PTB and UD benchmarks, and competitive performances to previous SOTA models on the CTB dataset. Code is available at https://github.com/ShannonAI/mrc-for-dependency-parsing.

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