Coreference Resolution without Span Representations

• URL: http://arxiv.org/abs/2101.00434v1
• Date: Sat, 2 Jan 2021 11:46:51 GMT
• Title: Coreference Resolution without Span Representations
• Authors: Yuval Kirstain, Ori Ram, Omer Levy
• Abstract summary: We introduce a lightweight coreference model that removes the dependency on span representations, handcrafted features, and NLPs. Our model performs competitively with the current end-to-end model, while being simpler and more efficient.
• Score: 20.84150608402576
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Since the introduction of deep pretrained language models, most task-specific NLP models were reduced to simple lightweight layers. An exception to this trend is the challenging task of coreference resolution, where a sophisticated end-to-end model is appended to a pretrained transformer encoder. While highly effective, the model has a very large memory footprint -- primarily due to dynamically-constructed span and span-pair representations -- which hinders the processing of complete documents and the ability to train on multiple instances in a single batch. We introduce a lightweight coreference model that removes the dependency on span representations, handcrafted features, and heuristics. Our model performs competitively with the current end-to-end model, while being simpler and more efficient.

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