HPCC-YNU at SemEval-2020 Task 9: A Bilingual Vector Gating Mechanism for Sentiment Analysis of Code-Mixed Text

TL;DR

This paper introduces a bilingual vector gating mechanism combined with attention for sentiment analysis of code-mixed social media text, outperforming baselines and achieving top rankings in SemEval-2020 Task 9.

Contribution

It proposes a novel bilingual vector gating mechanism that effectively integrates character and word-level information for code-mixed sentiment analysis.

Findings

Outperformed baseline algorithms in SemEval-2020 Task 9

Achieved fifth place in Spanglish and 19th in Hinglish

Demonstrated effectiveness of bilingual vector gating in multilingual sentiment tasks

Abstract

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