C oref QA : Coreference Resolution as Query-based Span Prediction (2020)

Abstract: In this paper, we present CorefQA, an accurate and extensible approach for the coreference resolution task. We formulate the problem as a span prediction task, like in question answering: A query is generated for each candidate mention using its surrounding context, and a span prediction module is employed to extract the text spans of the coreferences within the document using the generated query. This formulation comes with the following key advantages: (1) The span prediction strategy provides the flexibility of retrieving mentions left out at the mention proposal stage; (2) In the question answering framework, encoding the mention and its context explicitly in a query makes it possible to have a deep and thorough examination of cues embedded in the context of coreferent mentions; and (3) A plethora of existing question answering datasets can be used for data augmentation to improve the model’s generalization capability. Experiments demonstrate significant 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.

## introduction recent coreference resolution systems @xcite @xcite consider all text spans in a document 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 recovered since the downstream module only operates on the proposed mentions. existing models often suffer from mention proposal @xcite ., 1 https://github.com/shannonai/corefqa ## model in this section, we describe our corefqa model in detail. the overall architecture is illustrated in figure 2 . ## ablation study and analysis we perform comprehensive ablation studies and analyses on the conll-2012 development dataset. results are shown in ## conclusion in this paper, we present corefqa, a coreference resolution model that casts anaphora identification as the task of query-based span prediction in question answering. we showed that the proposed formalization can successfully retrieve mentions left out at the mention proposal stage. it also makes data augmentation using a plethora of existing question answering datasets possible. furthermore, a new speaker modeling strategy can also boost the performance in dialogue settings. empirical results on two widelyused coreference datasets demonstrate the effectiveness of our model. in future work, we will explore novel approaches to generate the questions based on each mention, and evaluate the influence of different question generation methods on the coreference resolution task.