Gendered Ambiguous Pronouns Shared Task: Boosting Model Confidence by Evidence Pooling

By Sandeep Attree. 2019

14 citations recorded by the source corpus.

This paper presents a strong set of results for resolving gendered ambiguous pronouns on the Gendered Ambiguous Pronouns shared task. The model presented here draws upon the strengths of state-of-the-art language and coreference resolution models, and introduces a novel evidence-based deep learning architecture. Injecting evidence from the coreference models compliments the base architecture, and analysis shows that the model is not hindered by their weaknesses, specifically gender bias. The modularity and simplicity of the architecture make it very easy to extend for further improvement and applicable to other NLP problems. Evaluation on GAP test data results in a state-of-the-art performance at 92.5% F1 (gender bias of 0.97), edging closer to the human performance of 96.6%. The end-to-end solution presented here placed 1st in the Kaggle competition, winning by a significant lead.

Subfields: Artificial Intelligence, Natural Language Processing

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Citations in the corpus

Language (Technology) is Power: A Critical Survey of “Bias” in NLP (2020, methodology): “…here; most propose techniques we would have categorized as “Questionable correlations,” with a few as “Other representational harms” (Abzaliev, 2019; Attree, 2019; Bao and Qiao, 2019; Chada, 2019; Ionita et al., 2019; Liu, 2019; Lois et al., 2019; Wang, 2019; Xu and Yang, 2019; Yang et al.,…”
Pre-training of Mention Representations in Coreference Models (2020, result): “There are however previous works (Attree, 2019; Chada, 2019) that consider the gold-two-mention task (Webster et al., 2018), where the locations of the gold names and pronoun are used during inference as well 1 .”