Unsupervised Self-Training for Sentiment Analysis of Code-Switched Data
Akshat Gupta, Sargam Menghani, Sai Krishna Rallabandi, Alan W. Black · 2021
Sentiment analysis is an important task in understanding social media content like customer reviews, Twitter and Facebook feeds etc.In multilingual communities around the world, a large amount of social media text is characterized by the presence of code-switching.Thus, it has become important to build models that can handle code-switched data.However, annotated code-switched data is scarce and there is a need for unsupervised models and algorithms.We propose a general framework called Unsupervised Self-Training and show its applications for the specific use case of sentiment analysis of code-switched data.We use the power of pre-trained BERT models for initialization and fine-tune them in an unsupervised manner, only using pseudo labels produced by zero-shot transfer.We test our algorithm on multiple code-switched languages and provide a detailed analysis of the learning dynamics of the algorithm with the aim of answering the question -'Does our unsupervised model understand the Code-Switched languages or does it just learn its representations?'.Our unsupervised models compete well with their supervised counterparts, with their performance reaching within 1-7% (weighted F1 scores) when compared to supervised models trained for a two class problem.