  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-PronounResolution
