Pipeline Coreference Resolution Model for Anaphoric Identity in Dialogues (2022)

Abstract: CODI-CRAC 2022 Shared Task in Dialogues consists of three sub-tasks: Sub-task 1 is the resolution of anaphoric identity, sub-task 2 is the resolution of bridging references, and sub-task 3 is the resolution of discourse deixis/abstract anaphora. Anaphora resolution is the task of detecting mentions from input documents and clustering the mentions of the same entity. The end-to-end model proceeds with the pruning of the candidate mention, and the pruning has the possibility of removing the correct mention. Also, the end-to-end anaphora resolution model has high model complexity, which takes a long time to train. Therefore, we proceed with the anaphora resolution as a two-stage pipeline model. In the first mention detection step, the score of the candidate word span is calculated, and the mention is predicted without pruning. In the second anaphora resolution step, the pair of mentions of the anaphora resolution relationship is predicted using the mentions predicted in the mention detection step. We propose a two-stage anaphora resolution pipeline model that reduces model complexity and training time, and maintains similar performance to end-to-end models. As a result of the experiment, the anaphora resolution showed a performance of 68.27% in Light, 48.87% in AMI, 69.06% in Persuasion, and 60.99% on Switchboard. Our final system ranked 3rd on the leaderboard of sub-task 1.

## introduction anaphora resolution @xcite is the task of detecting mentions from input documents and clustering the mentions of the same entity. it is used for various natural language processing tasks such as document summarization, question answering, and knowledge extraction. mention detection is the task of extracting candidate word spans that are likely to be mentions within a sentence. mention refers to a span of candidate words that are highly likely to have a anaphora relationship in a sentence. most of the anaphora resolution models being studied recently are end-to-end models. the end-to-end model extracts and learns all candidate word spans that are likely to be a mention and prunes them at a fixed ratio. the mention pairs are made from pruned mentions and are clustered into final mention pairs based on calculated scores. however, fixing the prune ratio is inefficient. a high pruning ratio increases the number of non-correct candidate mentions, increasing the amount and complexity of calculations. conversely, a low ratio increases the possibility of removing correct answers instead of lowering the amount and complexity. finding the optimal pruning ratio is important because the pruning ratio of the mention detection can directly affect the anaphora resolution performance. therefore, we propose a two-stage anaphora resolution pipeline model to speed up training and reduce model complexity without pruning. in the first mention detection step, the mention is trained by calculating scores of all possible candidate word spans in the input sentence. in the second anaphora resolution step, a mention pair consists of the mentions predicted in the detection step. then, the mention pair score is calculated to train the mention pair, which is a anaphora relationship. the proposed model shows high performance in the mention detection. moreover, compared with the self-reimplemented end-to-end anaphora resolution model, it shows similar performance and fast training speed. ## related works recently, anaphora resolution has been studied using an end-to-end model that learns pairwise scores of entity mentions @xcite . the end-toend model calculate mention score with all possible spans in a given text. the pruning step proceeds track resolution of anaphoric identities setting predicted mentions baseline -approach sec. 3.1 and 3.2 train data sec. 4.1 dev data sec. 4.1