An Accurate Model for Predicting the (Graded) Effect of Context in Word Similarity Based on Bert

3 May 2020 · Wei Bao, Hongshu Che, Jiandong Zhang ·

Natural Language Processing (NLP) has been widely used in the semantic analysis in recent years. Our paper mainly discusses a methodology to analyze the effect that context has on human perception of similar words, which is the third task of SemEval 2020. We apply several methods in calculating the distance between two embedding vector generated by Bidirectional Encoder Representation from Transformer (BERT). Our team will_go won the 1st place in Finnish language track of subtask1, the second place in English track of subtask1.

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Word Similarity

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Absolute Position Encodings • Adam • BPE • Dense Connections • Dropout • Label Smoothing • Layer Normalization • Linear Layer • Multi-Head Attention • Position-Wise Feed-Forward Layer • ReLU • Residual Connection • Scaled Dot-Product Attention • Softmax • Transformer

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An Accurate Model for Predicting the (Graded) Effect of Context in Word Similarity Based on Bert

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