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

## TL;DR

This paper introduces a novel evidence pooling architecture that enhances gendered ambiguous pronoun resolution, achieving state-of-the-art results and winning a Kaggle competition by effectively combining coreference models with deep learning.

## Contribution

The paper presents a new evidence-based deep learning model that improves gendered pronoun resolution by integrating coreference evidence, with a modular design for easy extension.

## Key findings

- Achieved 92.5% F1 on GAP test data
- State-of-the-art performance close to human level
- Won the Kaggle competition with a significant lead

## Abstract

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.