MSR India at SemEval-2020 Task 9: Multilingual Models Can Do Code-Mixing Too
Anirudh Srinivasan · 2020
In this paper, we present our system for the SemEval 2020 task on code-mixed sentiment analysis.Our system makes use of large transformer based multilingual embeddings like mBERT.Recent work has shown that these models posses the ability to solve code-mixed tasks in addition to their originally demonstrated cross-lingual abilities.We evaluate the stock versions of these models for the sentiment analysis task and also show that their performance can be improved by using unlabelled code-mixed data.Our submission (username Genius1237) achieved the second rank on the English-Hindi subtask with an F1 score of 0.726.