XLP at SemEval-2020 Task 9: Cross-lingual Models with Focal Loss for Sentiment Analysis of Code-Mixing Language

Proceedings of the 14th International Workshop on Semantic Evaluation, pages 975–980 Barcelona, Spain (Online), December 12, 2020. 975 XLP at SemEval-2020 Task 9: Cross-lingual Models with Focal Loss for Sentiment Analysis of Code-Mixing Language Yili Ma, Liang Zhao, Jie Hao OPPO Research Institute, Beijing, China {mayili,leonzhao,haojie}@oppo.com Abstract In this paper, we present an approach for sentiment analysis in code-mixed language on twitter defined in SemEval-2020 Task 9. Our team (referred as LiangZhao) employ different multilingual models with weighted loss focused on complexity of code-mixing in sentence, in which the best model achieved f1-score of 0.806 and ranked 1st of subtask- Sentimix Spanglish. The performance of method is analyzed and each component of our architecture is demonstrated. 1 Introduction Sentiment analysis is in the area of research that perform the automatic comprehension of the subjective information from user-generated data, which helps to gain the views on certain topics. Due to the rise of social media such as micro-blogs (e.g., Twitter) and the trend of global communications, they have accelerated the use of multilingual expressions, raising the concerns on code-mixing behavior (Patwa et al., 2020). To develop cross-lingual encoders that can encode any sentence into a shared embedding space, by using monolingual transfer learning, multilingual extensions of pretrained (Lample et al., 2019) encoders have been shown effective. As for code-mixed text, more complicated than cross-lingual sentence, it is crucial to consider the complexity of texts written in several different languages because different types of integration correlate with different social contexts (Gualberto A. et al., 2016). Sometimes, the user may post blogs in non-native language with grammar mistakes or even prefer to express the sentiment in the native language. The phenomena has encouraged the researchers to analyze the sentiment from multilingual code-mixed texts. Because Spanish and English share a lot of words with Latin roots, sometimes words with the same origin take a separate path in each language, or words with different origins resemble each other by coincidence, but have different meanings. For example, ´exito from Spanish means success, which resembles exit from English, with different meaning and sentiment. In the task, the number of words in a sentence vary from different languages dramatically. Intuitively, the language that has a bigger presence in the tweet would contain the sentiment of the sentence. To tackle the problem, we adopt the focal loss through calculating the ratio of each language in code-mixing text. The rest of the paper is structured as follows: Section 2 provides the detailed implementation method. Section 3 presents the results and performance of our models as well as experiment settings. Concluded remarks and future directions of our work are summarized in Section 4. 2 Implementation details 2.1 Preprocessing Normally, deep learning models have a simple data processing pipeline, while in the task data is very messy. Therefore we have used a more detailed method according to characteristic of the code-mixing data (URLs, emoji, hash symbol etc.) First, user name mentioned and URL are all removed because they are useless for sentiment …

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