← Back to papers
2020 COLING COLING 2020

MSR India at SemEval-2020 Task 9: Multilingual Models Can Do Code-Mixing Too

Abstract

AbstractIn 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.

🌉 Interdisciplinary Bridge — Artificial Intelligence and Natural Language Processing
🧭 Keyword Pioneer — transformer embedding
🐣 Hot Topic Early Bird — code-mixed language
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio

Authors

Anirudh Srinivasan

Topics

Artificial Intelligence > Core AI > Multimodal Learning Natural Language Processing > Understanding > Sentiment Analysis Natural Language Processing > Resources & Methods > Large Language Models Natural Language Processing > Resources & Methods > Multilingual NLP

Keywords

unsupervised learning sentiment analysis text classification code-mixed text multilingual model code-mixed language multilingual embedding multilingual transformer transformer embedding transformer model
Download PDF

Related papers

Persuasiveness of News Editorials depending on Ideology and Personality 2020
A Graph Representation of Semi-structured Data for Web Question Answering 2020
Span-based Joint Entity and Relation Extraction with Attention-based Span-specific and Contextual Semantic Representations 2020
Hierarchical Chinese Legal event extraction via Pedal Attention Mechanism 2020
End-to-End Emotion-Cause Pair Extraction with Graph Convolutional Network 2020