On GAP Coreference Resolution Shared Task: Insights from the 3rd Place Solution

By Artem Abzaliev. 2019

8 citations recorded by the source corpus.

This paper presents the 3rd-place-winning solution to the GAP coreference resolution shared task. The approach adopted consists of two key components: fine-tuning the BERT language representation model (Devlin et al., 2018) and the usage of external datasets during the training process. The model uses hidden states from the intermediate BERT layers instead of the last layer. The resulting system almost eliminates the difference in log loss per gender during the cross-validation, while providing high performance.

Subfields: Artificial Intelligence, Natural Language Processing, Evaluation & Benchmarks

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Citations in the corpus

Language (Technology) is Power: A Critical Survey of “Bias” in NLP (2020, methodology): “We cite the papers 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;…”
Gendered Ambiguous Pronoun (GAP) Shared Task at the Gender Bias in NLP Workshop 2019 (2019, background): “Attree (2019) for a good analysis), particularly when compared to hand-crafted features (Abzaliev, 2019).”