The Pipeline Model for Resolution of Anaphoric Reference and Resolution of Entity Reference (2021)

Abstract: The objective of anaphora resolution in dialogue shared-task is to go above and beyond the simple cases of coreference resolution in written text on which NLP has mostly focused so far, which arguably overestimate the performance of current SOTA models. The anaphora resolution in dialogue shared-task consists of three subtasks; subtask1, resolution of anaphoric identity and non-referring expression identification, subtask2, resolution of bridging references, and subtask3, resolution of discourse deixis/abstract anaphora. In this paper, we propose the pipelined model (i.e., a resolution of anaphoric identity and a resolution of bridging references) for the subtask1 and the subtask2. In the subtask1, our model detects mention via the parentheses prediction. Then, we yield mention representation using the token representation constituting the mention. Mention representation is fed to the coreference resolution model for clustering. In the subtask2, our model resolves bridging references via the MRC framework. We construct query for each entity as “What is related of ENTITY?”. The input of our model is query and documents(i.e., all utterances of dialogue). Then, our model predicts entity span that is answer for query.

## introduction coreference resolution (cr) is a task which identifying and clustering mentions referring to the same entity in text. multiple benchmark datasets of cr have been developed in recent years, however, most of these corpora only focus on identity coreference resolution and neglect relations like discourse deixis or bridging anaphora. the goal of * equal contribution the anaphora resolution in dialogue shared-task is to go above and beyond the simple cases of coreference resolution in these datasets @xcite . this shared-task consists of three subtasks; subtask1 for resolution of anaphoric identity and non-referring expression identification, subtask2 for identifying references to entities related to already introduced entities, and subtask3 for resolution of discourse deixis/abstract anaphora. the dataset consists conversations, so it might has grammatical error. to deal with this dataset, we need to perform speaker grounding of pronouns and focus on long-distance conversation structure. in this paper, we propose the pipelined model for the subtask1 and the subtask2 (i.e., a resolution of anaphoric identity and a resolution of bridging references). ## conclusion we proposed the pipeline model for resolution of anaphoric reference and resolution of entity reference. our resolution of anaphoric reference model consists of mention detection model and coreference resolution model. mention detection model extracts mentions via the parentheses prediction. based on the result of mention detection model, the mention representation is created and then is fed to the coreference resolution model. our coreference resolution model points to the closest mention using pointer network, and then merges mentions in same cluster sequentially. in subtask1, our model achieved 65.9% the overall average score of all datasets (ranked at top 3). our resolution of entity reference model utilizes a mrc framework. given a query for related entity, our model predicts the related entity span. our model achieved the overall average score 11.76% (ranked at top 2) and 17.26% (ranked at top 2) in eval-br(pred) and eval-br(gold) respectively.