@inproceedings{bao-etal-2020-will,
    title = "{W}ill{\_}{G}o at {S}em{E}val-2020 Task 3: An Accurate Model for Predicting the (Graded) Effect of Context in Word Similarity Based on {BERT}",
    author = "Bao, Wei  and
      Che, Hongshu  and
      Zhang, Jiandong",
    editor = "Herbelot, Aurelie  and
      Zhu, Xiaodan  and
      Palmer, Alexis  and
      Schneider, Nathan  and
      May, Jonathan  and
      Shutova, Ekaterina",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2020.semeval-1.38/",
    doi = "10.18653/v1/2020.semeval-1.38",
    pages = "301--306",
    abstract = "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."
}