Sentiment classification is a fundamental NLP task of detecting the sentiment polarity of a given text. In this paper we show how solving sentiment span extraction as an auxiliary task can help improve final sentiment classification performance in a low-resource code-mixed setup. To be precise, we don’t solve a simple multi-task learning objective, but rather design a unified transformer framework that exploits the bidirectional connection between the two tasks simultaneously. To facilitate research in this direction we release gold-standard human-annotated sentiment span extraction dataset for Tamil-english code-switched texts. Extensive experiments and strong baselines show that our proposed approach outperforms sentiment and span prediction by 1.27% and 2.78% respectively when compared to the best performing MTL baseline. We also establish the generalizability of our approach on the Twitter Sentiment Extraction dataset. We make our code and data publicly available on GitHub

Social network platforms are generally used to share positive, constructive, and insightful content. However, in recent times, people often get exposed to objectionable content like threat, identity attacks, hate speech, insults, obscene texts, offensive remarks or bullying. Existing work on toxic speech detection focuses on binary classification or on differentiating toxic speech among a small set of categories. This paper describes the system proposed by team Cisco for SemEval-2021 Task 5: Toxic Spans Detection, the first shared task focusing on detecting the spans in the text that attribute to its toxicity, in English language. We approach this problem primarily in two ways: a sequence tagging approach and a dependency parsing approach. In our sequence tagging approach we tag each token in a sentence under a particular tagging scheme. Our best performing architecture in this approach also proved to be our best performing architecture overall with an F1 score of 0.6922, thereby placing us 7th on the final evaluation phase leaderboard. We also explore a dependency parsing approach where we extract spans from the input sentence under the supervision of target span boundaries and rank our spans using a biaffine model. Finally, we also provide a detailed analysis of our results and model performance in our paper.