Understanding Mention Detector-Linker Interaction for Neural Coreference Resolution
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Coreference resolution is an important task for discourse-level natural language understanding. However, despite significant recent progress, the quality of current state-of-the-art systems still considerably trails behind human-level performance. Using the CoNLL-2012 and PreCo datasets, we dissect the best instantiation of the mainstream end-to-end coreference resolution model that underlies most current best-performing coreference systems, and empirically analyze the behavior of its two components: the mention detector and mention linker. While the detector traditionally focuses heavily on recall as a design decision, we demonstrate the importance of precision, calling for their balance. However, we point out the difficulty in building a precise detector due to its inability to make important anaphoricity decisions. We also highlight the enormous room for improving the linker and that the rest of its errors mainly involve pronoun resolution. We hope our findings will help future research in building coreference resolution systems.
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