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Found 8 papers, 1 papers with code
Neural Anaphora Resolution in Dialogue
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.
The CODI-CRAC 2021 Shared Task on Anaphora, Bridging, and Discourse Deixis Resolution in Dialogue: A Cross-Team Analysis
The CODI-CRAC 2021 shared task is the first shared task that focuses exclusively on anaphora resolution in dialogue and provides three tracks, namely entity coreference resolution, bridging resolution, and discourse deixis resolution.
Constrained Multi-Task Learning for Bridging Resolution
We examine the extent to which supervised bridging resolvers can be improved without employing additional labeled bridging data by proposing a novel constrained multi-task learning framework for bridging resolution, within which we (1) design cross-task consistency constraints to guide the learning process; (2) pre-train the entity coreference model in the multi-task framework on the large amount of publicly available coreference data; and (3) integrating prior knowledge encoded in rule-based resolvers.
End-to-End Neural Bridging Resolution
The state of bridging resolution research is rather unsatisfactory: not only are state-of-the-art resolvers evaluated in unrealistic settings, but the neural models underlying these resolvers are weaker than those used for entity coreference resolution.
Neural Anaphora Resolution in Dialogue Revisited
We present the systems that we developed for all three tracks of the CODI-CRAC 2022 shared task, namely the anaphora resolution track, the bridging resolution track, and the discourse deixis resolution track.
Analyzing Coreference and Bridging in Product Reviews
Product reviews may have complex discourse including coreference and bridging relations to a main product, competing products, and interacting products.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
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Bridging Resolution: Making Sense of the State of the Art
While Yu and Poesio (2020) have recently demonstrated the superiority of their neural multi-task learning (MTL) model to rule-based approaches for bridging anaphora resolution, there is little understanding of (1) how it is better than the rule-based approaches (e. g., are the two approaches making similar or complementary mistakes?)
Bridging Resolution: A Survey of the State of the Art
Bridging reference resolution is an anaphora resolution task that is arguably more challenging and less studied than entity coreference resolution.
