Neural Anaphora Resolution in Dialogue (2021)

Abstract: We describe the systems that we developed for the three tracks of the CODI-CRAC 2021 shared task, namely entity coreference resolution, bridging resolution, and discourse deixis resolution. Our team ranked second for entity coreference resolution, first for bridging resolution, and first for discourse deixis resolution.

## introduction the codi-crac 2021 shared task @xcite , which focuses on anaphora resolution in dialogue, provides three tracks, namely entity coreference resolution, bridging resolution, and discourse deixis/abstract anaphora resolution. while the crac 2018 shared task @xcite provides the same three tracks, the two shared tasks differ by the genre they focus on: crac 2018 focuses primarily on text, whereas codi-crac 2021 focuses exclusively on spoken dialogue. not only has entity coreference resolution been an active area of research in the nlp community in the past few decades, but recent years have seen considerable progress on entity coreference because of the development of span-based neural models @xcite . compared to entity coreference, bridging resolution and discourse deixis resolution are much less studied, and hence they are arguably the more interesting tracks of this shared task. in particular, a relevant question is: can the successes of span-based models be extended from entity coreference to bridging resolution and discourse deixis resolution? we participated in all three tracks of the shared task. for bridging and discourse deixis resolution, we submitted results based on both predicted mentions and gold mentions. given the recent successes of span-based neural entity coreference models, which can learn task-specific representations * *equal contribution of text spans, we use them as our starting point for all three tracks. specifically: a brief overview of the approaches we adopted for the three tracks can be found in the rest of the paper is structured as follows. the next three sections describe our work for the three tracks, namely entity coreference (section 2), discourse deixis (section 3), and bridging (section 4). within each section, we describe our approach, our official results, and some quantitative analysis. we present our conclusions in section 5. ## entity coreference resolution to build our entity coreference system, we use as our baseline coref-hoi, which is xu and choi's (2020) coreference model. below we first give an overview of coref-hoi and then describe our extensions to it. ## discourse deixis resolution we cast discourse deixis resolution as identity anaphora resolution. this allows us to continue to use the xu and choi (2020) entity coreference model as the baseline. the shared task divides the evaluation of discourse deixis resolution into two phases: (1) the predicted phase, where a system needs to first identify all of the entity mentions that likely correspond to anaphors and antecedents, then perform discourse deixis resolution on the predicted mentions; and (2) the gold phase, which is essentially the same as the predicted phase except that the mentions corresponding to anaphors are to be extracted from the given gold mentions. below we first describe the system we developed for the predicted phase and then show how the system we used for the gold phase differs from the one we used for the predicted phase. as we will see shortly, our models for both phases perform joint mention extraction and discourse deixis resolution. ## bridging resolution the shared task divides the evaluation of bridging anaphora resolution into two phases: (1) the predicted phase, where a system needs to first identify all of the entity mentions that likely correspond to anaphors and antecedents, then perform bridging resolution on the predicted mentions; and (2) the gold phase, which is essentially the same as the predicted phase except that bridging resolution is performed on the given gold mentions. below we describe our approach and our official test results. ## conclusions we presented our systems we developed for the three tracks of the codi-crac 2021 shared task, namely entity coreference resolution, discourse deixis resolution, and bridging resolution. our team ranked second for entity coreference resolution, first for discourse deixis resolution, and first for bridging resolution. for entity coreference resolution, our analysis of our three approaches (pipeline, end-to-end, as well as its "no constraint" variant) revealed that there is a perfect correlation between entity coreference performance and mention detection, suggesting that coreference performance can further be improved by improving mention detection. for discourse deixis resolution, our analysis revealed that (1) contrary to common wisdom, anaphor detection was not always easier in the gold setting (where gold mentions were given) than the predicted setting; and (2) substantial gains could be achieved simply by removing the singleton clusters from the output prior to scoring, especially in the predicted setting. for bridging resolution, our results suggested that the gold setting is easier than the predicted setting, and our analysis showed that the sieves, anaphor detection, and recall-precision balancing all contributed positively to overall system performance. finally, for a fur