Damrin Kim


2022

pdf
Pipeline Coreference Resolution Model for Anaphoric Identity in Dialogues
Damrin Kim | Seongsik Park | Mirae Han | Harksoo Kim
Proceedings of the CODI-CRAC 2022 Shared Task on Anaphora, Bridging, and Discourse Deixis in Dialogue

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.

2021

pdf
The Pipeline Model for Resolution of Anaphoric Reference and Resolution of Entity Reference
Hongjin Kim | Damrin Kim | Harksoo Kim
Proceedings of the CODI-CRAC 2021 Shared Task on Anaphora, Bridging, and Discourse Deixis in Dialogue

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.