Zhestyatsky at SemEval-2021 Task 2: ReLU over Cosine Similarity for BERT Fine-tuning

• URL: http://arxiv.org/abs/2104.06439v1
• Date: Tue, 13 Apr 2021 18:28:58 GMT
• Title: Zhestyatsky at SemEval-2021 Task 2: ReLU over Cosine Similarity for BERT Fine-tuning
• Authors: Boris Zhestiankin and Maria Ponomareva
• Abstract summary: This paper presents our contribution to SemEval-2021 Task 2: Multilingual and Cross-lingual Word-in-Context Disambiguation (MCL-WiC) Our experiments cover English (EN-EN) sub-track from the multilingual setting of the task. We find the combination of Cosine Similarity and ReLU activation leading to the most effective fine-tuning procedure.
• Score: 0.07614628596146598
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper presents our contribution to SemEval-2021 Task 2: Multilingual and Cross-lingual Word-in-Context Disambiguation (MCL-WiC). Our experiments cover English (EN-EN) sub-track from the multilingual setting of the task. We experiment with several pre-trained language models and investigate an impact of different top-layers on fine-tuning. We find the combination of Cosine Similarity and ReLU activation leading to the most effective fine-tuning procedure. Our best model results in accuracy 92.7%, which is the fourth-best score in EN-EN sub-track.

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