Pronoun resolution is part of coreference resolution, the task of pairing an expression to its referring entity. This is an important task for natural language understanding and a necessary component of machine translation systems, chat bots and assistants. Neural machine learning systems perform far from ideally in this task, reaching as low as 73 F1 scores on modern benchmark datasets. Moreover, they tend to perform better for masculine pronouns than for feminine ones. Thus, the problem is both challenging and important for NLP researchers and practitioners. In this project, we describe our BERT-based approach to solving the problem of gender-balanced pronoun resolution. We are able to reach 92 F1 score and a much lower gender bias on the benchmark dataset shared by Google AI Language team.
Among popular approaches to coreference resolution are: https: bit.ly 2JbKxv1 rule-based, mention pair, mention ranking, and clustering. As for rule-based approaches, they describe naïve Hobbs algorithm @cite_12 which, in spite of being naïve, has shown state-of-the-art performance on the OntoNotes dataset https: catalog.ldc.upenn.edu LDC2013T19 up to 2010.
@cite_12
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The book presents papers on natural language processing, focusing on the central issues of representation, reasoning, and recognition. The introduction discusses theoretical issues, historical developments, and current problems and approaches. The book presents work in syntactic models (parsing and grammars), semantic interpretation, discourse interpretation, language action and intentions, language generation, and systems.