Coreference Resolution for Ambiguous Pronoun with BERT and MLP

The process of identifying all the expressions in a text which refer to the same real-world entity is known as coreference resolution. It is one of the main challenges that are faced in Natural Language Processing. We are focusing on creating a better approach for the task of ambiguous pronoun resolution. We are proposing to make use of a BERT-based approach which makes use of PyTorch pre-trained BERT and PyTorch helper bot along with a custom-made MultiLayerPerceptron model as a classifier to solve this problem. We are using the dataset released by Google AI called Gendered Ambiguous Pronouns. The contextual embedding is received by training the preprocessed data with Pretrained BERT and then the contextual embeddings are passed to the MLP Classifier which is used for classification purposes to get results for coreference resolution for target pronoun.