Wei Bao


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Will_Go at SemEval-2020 Task 3: An Accurate Model for Predicting the (Graded) Effect of Context in Word Similarity Based on BERT
Wei Bao | Hongshu Che | Jiandong Zhang
Proceedings of the Fourteenth Workshop on Semantic Evaluation

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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Will_go at SemEval-2020 Task 9: An Accurate Approach for Sentiment Analysis on Hindi-English Tweets Based on Bert and Pesudo Label Strategy
Wei Bao | Weilong Chen | Wei Bai | Yan Zhuang | Mingyuan Cheng | Xiangyu Ma
Proceedings of the Fourteenth Workshop on Semantic Evaluation

Mixing languages are widely used in social media, especially in multilingual societies like India. Detecting the emotions contained in these languages, which is of great significance to the development of society and political trends. In this paper, we propose an ensemble of pesudo-label based Bert model and TFIDF based SGDClassifier model to identify the sentiments of Hindi-English (Hi-En) code-mixed data. The ensemble model combines the strengths of rich semantic information from the Bert model and word frequency information from the probabilistic ngram model to predict the sentiment of a given code-mixed tweet. Finally our team got an average F1 score of 0.731 on the final leaderboard,and our codalab username is will_go.


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Tibetan-Chinese Neural Machine Translation based on Syllable Segmentation
Wen Lai | Xiaobing Zhao | Wei Bao
Proceedings of the AMTA 2018 Workshop on Technologies for MT of Low Resource Languages (LoResMT 2018)