4,226 research outputs found
Light Coreference Resolution for Russian with Hierarchical Discourse Features
Coreference resolution is the task of identifying and grouping mentions
referring to the same real-world entity. Previous neural models have mainly
focused on learning span representations and pairwise scores for coreference
decisions. However, current methods do not explicitly capture the referential
choice in the hierarchical discourse, an important factor in coreference
resolution. In this study, we propose a new approach that incorporates
rhetorical information into neural coreference resolution models. We collect
rhetorical features from automated discourse parses and examine their impact.
As a base model, we implement an end-to-end span-based coreference resolver
using a partially fine-tuned multilingual entity-aware language model LUKE. We
evaluate our method on the RuCoCo-23 Shared Task for coreference resolution in
Russian. Our best model employing rhetorical distance between mentions has
ranked 1st on the development set (74.6% F1) and 2nd on the test set (73.3% F1)
of the Shared Task. We hope that our work will inspire further research on
incorporating discourse information in neural coreference resolution models.Comment: Accepted at Dialogue-2023 conferenc
Conundrums in noun phrase coreference resolution: making sense of the state-of-the-art
Journal ArticleWe aim to shed light on the state-of-the-art in NP coreference resolution by teasing apart the differences in the MUC and ACE task definitions, the assumptions made in evaluation methodologies, and inherent differences in text corpora. First, we examine three subproblems that play a role in coreference resolution: named entity recognition, anaphoricity determination, and coreference element detection. We measure the impact of each subproblem on coreference resolution and confirm that certain assumptions regarding these subproblems in the evaluation methodology can dramatically simplify the overall task. Second, we measure the performance of a state-of-the-art coreference resolver on several classes of anaphora and use these results to develop a quantitative measure for estimating coreference resolution performance on new data sets
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