Training Bi-Encoders for Word Sense Disambiguation

Harsh Kohli

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arXiv · 2105.10146

Training Bi-Encoders for Word Sense Disambiguation

Harsh Kohli

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Abstract

Modern transformer-based neural architectures yield impressive results in nearly every NLP task and Word Sense Disambiguation, the problem of discerning the correct sense of a word in a given context, is no exception. State-of-the-art approaches in WSD today leverage lexical information along with pre-trained embeddings from these models to achieve results comparable to human inter-annotator agreement on standard evaluation benchmarks. In the same vein, we experiment with several strategies to optimize bi-encoders for this specific task and propose alternative methods of presenting lexical information to our model. Through our multi-stage pre-training and fine-tuning pipeline we further the state of the art in Word Sense Disambiguation.

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Harsh Kohli. 2021-05-21. Training Bi-Encoders for Word Sense Disambiguation. https://arxiv.org/abs/2105.10146

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