The presence of large-scale corpora for Natural Language Inference (NLI) has spurred deep learning research in this area, though much of this research has focused solely on monolingual data. Code-mixing is the intertwined usage of multiple languages, and is commonly seen in informal conversations among polyglots. Given the rising importance of dialogue agents, it is imperative that they understand code-mixing, but the scarcity of code-mixed Natural Language Understanding (NLU) datasets has precluded research in this area. The dataset by Khanuja et al. (2020a) for detecting conversational entailment in codemixed Hindi-English text is the first of its kind. We investigate the effectiveness of language modeling, data augmentation, translation, and architectural approaches to address the codemixed, conversational, and low-resource aspects of this dataset. We obtain +8.09% test set accuracy over the current state of the art.

The rise in the usage of social media has placed it in a central position for news dissemination and consumption. This greatly increases the potential for proliferation of rumours and misinformation. In an effort to mitigate the spread of rumours, we tackle the related task of identifying the stance (Support, Deny, Query, Comment) of a social media post. Unlike previous works (Fajcik et al., 2019;Yang et al., 2019), we impose inductive biases that capture platform specific user behavior. These biases, coupled with social media finetuning of BERT allow for better language understanding, thus yielding an F 1 score of 58.7 on the SemEval 2019 task on rumour stance detection.