Sociolinguistically Informed Interpretability: A Case Study on Hinglish Emotion Classification
This addresses interpretability in emotion classification for Hinglish speakers, but it is incremental as it applies existing methods to a specific domain.
The study investigated whether pre-trained language models learn sociolinguistic associations between language choice and emotional expression in Hinglish, finding that models do learn these patterns but may overgeneralize them to infrequent examples.
Emotion classification is a challenging task in NLP due to the inherent idiosyncratic and subjective nature of linguistic expression, especially with code-mixed data. Pre-trained language models (PLMs) have achieved high performance for many tasks and languages, but it remains to be seen whether these models learn and are robust to the differences in emotional expression across languages. Sociolinguistic studies have shown that Hinglish speakers switch to Hindi when expressing negative emotions and to English when expressing positive emotions. To understand if language models can learn these associations, we study the effect of language on emotion prediction across 3 PLMs on a Hinglish emotion classification dataset. Using LIME and token level language ID, we find that models do learn these associations between language choice and emotional expression. Moreover, having code-mixed data present in the pre-training can augment that learning when task-specific data is scarce. We also conclude from the misclassifications that the models may overgeneralise this heuristic to other infrequent examples where this sociolinguistic phenomenon does not apply.