MSnet: A BERT-based Network for Gendered Pronoun Resolution
By Zili Wang. 2019
4 citations recorded by the source corpus.
The pre-trained BERT model achieves a remarkable state of the art across a wide range of tasks in natural language processing. For solving the gender bias in gendered pronoun resolution task, I propose a novel neural network model based on the pre-trained BERT. This model is a type of mention score classifier and uses an attention mechanism with no parameters to compute the contextual representation of entity span, and a vector to represent the triple-wise semantic similarity among the pronoun and the entities. In stage 1 of the gendered pronoun resolution task, a variant of this model, trained in the fine-tuning approach, reduced the multi-class logarithmic loss to 0.3033 in the 5-fold cross-validation of training set and 0.2795 in testing set. Besides, this variant won the 2nd place with a score at 0.17289 in stage 2 of the task. The code in this paper is available at: https://github.com/ziliwang/MSnet-for-Gendered-Pronoun-Resolution
Subfields: Natural Language Processing
How researchers see this paper
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In Natural Language Processing
- Hal Daumé Iii
- Su Lin Blodgett
- Hanna M. Wallach
- Solon Barocas
- Yulia Tsvetkov
- Yejin Choi
- Emilio Ferrara
- Sunipa Dev
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., 2019).”