论文标题
Semeval-2020任务9：一种双语矢量门控机制，用于代码混合文本的情感分析
HPCC-YNU at SemEval-2020 Task 9: A Bilingual Vector Gating Mechanism for Sentiment Analysis of Code-Mixed Text
论文作者
论文摘要
在社交媒体平台上使用代码混合来表达多语言社会的意见和情感是很常见的。此任务的目的是检测代码混合社交媒体文本的观点。代码混合文本对传统的NLP系统构成了巨大的挑战，该系统目前使用单语言资源来处理多语言混合问题。过去，该任务在各自的情感词典中使用词典查找，并使用长期记忆（LSTM）神经网络进行单语资源来解决。在本文中，我们（我的Codalab用户名是Kongjun）提出了一个系统，该系统使用双语矢量门控机制来完成任务。该模型由两个主要部分组成：矢量门控机制，结合了角色和单词级别，以及注意力机制，它提取了文本的重要情感部分。结果表明，所提出的系统的表现优于基线算法。我们在Spanglish中获得了第五名，在Hinglish中获得了第19位。
It is fairly common to use code-mixing on a social media platform to express opinions and emotions in multilingual societies. The purpose of this task is to detect the sentiment of code-mixed social media text. Code-mixed text poses a great challenge for the traditional NLP system, which currently uses monolingual resources to deal with the problem of multilingual mixing. This task has been solved in the past using lexicon lookup in respective sentiment dictionaries and using a long short-term memory (LSTM) neural network for monolingual resources. In this paper, we (my codalab username is kongjun) present a system that uses a bilingual vector gating mechanism for bilingual resources to complete the task. The model consists of two main parts: the vector gating mechanism, which combines the character and word levels, and the attention mechanism, which extracts the important emotional parts of the text. The results show that the proposed system outperforms the baseline algorithm. We achieved fifth place in Spanglish and 19th place in Hinglish.The code of this paper is availabled at : https://github.com/JunKong5/Semveal2020-task9
