A Comparative Study of Transformers on Word Sense Disambiguation

• URL: http://arxiv.org/abs/2111.15417v1
• Date: Tue, 30 Nov 2021 14:10:22 GMT
• Title: A Comparative Study of Transformers on Word Sense Disambiguation
• Authors: Avi Chawla and Nidhi Mulay and Vikas Bishnoi and Gaurav Dhama and Dr. Anil Kumar Singh
• Abstract summary: This paper presents a comparative study on the contextualization power of neural network-based embedding systems. We evaluate their contextualization power using two sample Word Sense Disambiguation (WSD) tasks, SensEval-2 and SensEval-3. Experimental results show that the proposed techniques also achieve superior results over the current state-of-the-art on both the WSD tasks.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent years of research in Natural Language Processing (NLP) have witnessed dramatic growth in training large models for generating context-aware language representations. In this regard, numerous NLP systems have leveraged the power of neural network-based architectures to incorporate sense information in embeddings, resulting in Contextualized Word Embeddings (CWEs). Despite this progress, the NLP community has not witnessed any significant work performing a comparative study on the contextualization power of such architectures. This paper presents a comparative study and an extensive analysis of nine widely adopted Transformer models. These models are BERT, CTRL, DistilBERT, OpenAI-GPT, OpenAI-GPT2, Transformer-XL, XLNet, ELECTRA, and ALBERT. We evaluate their contextualization power using two lexical sample Word Sense Disambiguation (WSD) tasks, SensEval-2 and SensEval-3. We adopt a simple yet effective approach to WSD that uses a k-Nearest Neighbor (kNN) classification on CWEs. Experimental results show that the proposed techniques also achieve superior results over the current state-of-the-art on both the WSD tasks

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