  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. The code
is available at https://github.com/sattree/gap.
