Training Bi-Encoders for Word Sense Disambiguation

• URL: http://arxiv.org/abs/2105.10146v1
• Date: Fri, 21 May 2021 06:06:03 GMT
• Title: Training Bi-Encoders for Word Sense Disambiguation
• Authors: Harsh Kohli
• Abstract summary: State-of-the-art approaches in Word Sense Disambiguation leverage lexical information along with pre-trained embeddings from these models to achieve results comparable to human inter-annotator agreement on standard evaluation benchmarks. We further the state of the art in Word Sense Disambiguation through our multi-stage pre-training and fine-tuning pipeline.
• Score: 4.149972584899897
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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