CorefQA: Coreference Resolution as Query-based Span Prediction

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 6953–6963 July 5 - 10, 2020. c⃝2020 Association for Computational Linguistics 6953 CorefQA: Coreference Resolution as Query-based Span Prediction Wei Wu♣, Fei Wang♣, Arianna Yuan♦♣, Fei Wu♠ and Jiwei Li♠♣ ♠ Department of Computer Science and Technology, Zhejiang University ♦ Computer Science Department, Stanford University ♣ ShannonAI xfyuan@stanford.edu, wufei@zju.edu.cn {wei wu, fei wang,jiwei li}@shannonai.com Abstract In this paper, we present CorefQA, an accu- rate and extensible approach for the corefer- ence resolution task. We formulate the prob- lem as a span prediction task, like in ques- tion answering: A query is generated for each candidate mention using its surrounding con- text, and a span prediction module is em- ployed to extract the text spans of the corefer- ences within the document using the generated query. This formulation comes with the fol- lowing key advantages: (1) The span predic- tion strategy provides the flexibility of retriev- ing mentions left out at the mention proposal stage; (2) In the question answering frame- work, encoding the mention and its context ex- plicitly in a query makes it possible to have a deep and thorough examination of cues em- bedded in the context of coreferent mentions; and (3) A plethora of existing question an- swering datasets can be used for data augmen- tation to improve the model’s generalization capability. Experiments demonstrate signifi- cant performance boost over previous models, with 83.1 (+3.5) F1 score on the CoNLL-2012 benchmark and 87.5 (+2.5) F1 score on the GAP benchmark. 1 1 Introduction Recent coreference resolution systems (Lee et al., 2017, 2018; Zhang et al., 2018a; Kantor and Globerson, 2019) consider all text spans in a doc- ument as potential mentions and learn to find an antecedent for each possible mention. There are two key issues with this paradigm, in terms of task formalization and the algorithm. At the task formalization level, mentions left out at the mention proposal stage can never be recov- ered since the downstream module only operates on the proposed mentions. Existing models of- ten suffer from mention proposal (Zhang et al., 1https://github.com/ShannonAI/CorefQA Original Passage In addition , many people were poisoned when toxic gas was released. They were poi- soned and did not know how to protect them- selves against the poison. Our formulation Q1: Who were poisoned when toxic gas was released? A1: [They, themselves] Q2: What was released when many people were poisoned? A2: [the poison] Q3: Who were poisoned and did not know how to protect themselves against the poison? A3: [many people, themselves] Q4: Whom did they not know how to protect against the poison? A4: [many people, They] Q5: They were poisoned and did not know how to protect themselves against what? A5: [toxic gas] Figure 1: An illustration of the paradigm shift from coreference resolution to query-based span prediction. Spans with the same format represent coreferent men- tions. 2018a). The coreference datasets can only pro- vide a weak signal for spans that correspond to …

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