Adapted End-to-End Coreference Resolution System for Anaphoric Identities in Dialogues (2021)

Abstract: We present an effective system adapted from the end-to-end neural coreference resolution model, targeting on the task of anaphora resolution in dialogues. Three aspects are specifically addressed in our approach, including the support of singletons, encoding speakers and turns throughout dialogue interactions, and knowledge transfer utilizing existing resources. Despite the simplicity of our adaptation strategies, they are shown to bring significant impact to the final performance, with up to 27 F1 improvement over the baseline. Our final system ranks the 1st place on the leaderboard of the anaphora resolution track in the CRAC 2021 shared task, and achieves the best evaluation results on all four datasets.

## introduction coreference resolution of anaphoric identities (a.k.a. anaphora resolution) is a long-studied natural language processing (nlp) task, and is still considered one of the unsolved problems, as it demands deep semantic understanding as well as world knowledge. although there is a significant performance boost recently by the neural decoders @xcite and deep contextualized encoders such as bert and spanbert @xcite , the majority of the experiments are based on @xcite from the conll 2012 shared task, which may overestimate the model performance due to two perspectives: the lack of support for harder cases such as singletons and split-antecedents, and the lack of focus on real-world dialogues. in this work, we target on the task of anaphora resolution in the crac 2021 shared task @xcite ) that addresses both perspectives, and present an effective coreference resolution system that is adapted from the recent end-to-end coreference model. all datasets in the crac 2021 shared task are in the universal anaphora format. for simplicity, we refer to it as the ua format, and refer to the annotation scheme of the conll 2012 shared task as the conll format. the ua format is an extension of the conll format, and further supports bridging references and discourse deixis. for anaphora resolution, the ua format differs from the conll format on three aspects: the support of singletons, split-antecedents, and non-referring expressions (excluded from the current evaluation). our approach specifically addresses the singleton problem (section 3.1), which is shown to be a critical component under the ua setting that brings 17-22 f1 improvement on all datasets (section 5.2). few recent work has studied the split-antecedent problem @xcite , and we leave the split-antecedents as future work. in addition to singletons, our approach also emphasizes on the speaker encoding (section 3.3) and knowledge transfer (section 3.4) to address the dialogue-domain perspective. especially, we use a simple strategy of speaker-augmented encoding that captures the speaker interaction and dialogueturn information, utilizing the strong transformers encoder. it has been shown by the previous study that conversational metadata such as speakers can be significant for coreference resolution on dialogue documents @xcite , and we do see 2-3 f1 improvement on three datasets by simply applying the speaker encoding strategy (section 5.3). knowledge transfer from other existing resources is also shown to be important in our approach. two different strategies are experimented, and the domain-adaptation strategy is able to bring large improvement, boosting 8 f1 for light and 6 f1 on psua ( our final system ranks the 1st place on the leaderboard of the anaphora resolution track in the crac 2021 shared task, and achieves the best evaluation results on all four datasets, with 63.96 f1 for ami, 80.33 f1 for light, 78.41 f1 for psua, 74.49 f1 for swbd (section 5.1). a brief summary of our final submission is shown in ## related work pretrained transformers encoders have been successfully adopted by recent coreference resolution models and shown significant improvement @xcite . we also adopt the transformers encoder in our approach because of its superior performance. for the neural decoder, there have been two popular directions from recent work. one is mention-ranking-based, where the model predicts only one antecedent for each mention without focusing on the cluster structure @xcite @xcite . the other is cluster-based, where the model maintains the predicted clusters and performs cluster merging @xcite @xcite . we adopt the mention-ranking framework in our approach because of its simplicity as well as its state-of-the-art decoding performance. ## results and analysis the universal anaphora scorer 1 is used in the official evaluation process. for the task of anaphora resolution, the main evaluation metric is the averaged f1 score of muc, b 3 and ceaf φ 4 , same as the conll 2012 shared task. singletons and splitantecedents are included in the evaluation, while non-referring expressions are excluded. ## conclusion in this work, we present an adapted end-to-end coreference resolution system for anaphoric identities in dialogues, specifically addressing three aspects: the support for singletons, stronger speaker and turn encoding through the dialogue interactions, as well as the knowledge transfer utilizing other existing resources. our final system achieves the best results on all four datasets on the leaderboard of the crac 2021 shared task, and further analysis is performed to show the effectiveness of our proposed adaptation strategies.