Deep Learning Brasil -- NLP at SemEval-2020 Task 9: Overview of Sentiment Analysis of Code-Mixed Tweets
arXiv:2008.01544
Abstract
In this paper, we describe a methodology to predict sentiment in code-mixed tweets (hindi-english). Our team called verissimo.manoel in CodaLab developed an approach based on an ensemble of four models (MultiFiT, BERT, ALBERT, and XLNET). The final classification algorithm was an ensemble of some predictions of all softmax values from these four models. This architecture was used and evaluated in the context of the SemEval 2020 challenge (task 9), and our system got 72.7% on the F1 score.
References in corpus (6)
- ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
- Quasi-Recurrent Neural Networks
- Sentiment Analysis of Code-Mixed Indian Languages: An Overview of SAIL_Code-Mixed Shared Task @ICON-2017
- Sentiment Identification in Code-Mixed Social Media Text
- A Corpus of English-Hindi Code-Mixed Tweets for Sarcasm Detection
- JU_KS@SAIL_CodeMixed-2017: Sentiment Analysis for Indian Code Mixed Social Media Texts