A common approach to interpreting multilingual language models is to evaluate their internal representations. For example, studies have found that languages occupy distinct subspaces in the models’ representation spaces, and geometric distances between languages often reflect linguistic properties such as language families and typological features. In our work, we investigate whether geometric distances between language representations correlate with zero-shot crosslingual transfer performance for POS-tagging and NER in three multilingual language models. We consider four distance metrics, including new metrics that identify a basis for a multilingual representation space that sorts axes based on their language-separability. We find that each distance metric either only moderately correlates or does not correlate with crosslingual transfer performance, and metrics do not generalize well across models, layers, and tasks. Although pairwise language separability is a reasonable predictor of crosslingual transfer, representational geometry overall is an inconsistent predictor for the crosslingual performance of multilingual language models.

The use of Natural Language Processing (NLP) for Automated Essay Scoring (AES) has been well explored in the English language, with benchmark models exhibiting performance comparable to human scorers. However, AES in Hindi and other low-resource languages remains unexplored. In this study, we reproduce and compare state-of-the-art methods for AES in the Hindi domain. We employ classical feature-based Machine Learning (ML) and advanced end-to-end models, including LSTM Networks and Fine-Tuned Transformer Architecture, in our approach and derive results comparable to those in the English language domain. Hindi being a low-resource language, lacks a dedicated essay-scoring corpus. We train and evaluate our models using translated English essays and empirically measure their performance on our own small-scale, real-world Hindi corpus. We follow this up with an in-depth analysis discussing prompt-specific behavior of different language models implemented.