22 citations · 26 across the 5 of their papers we have counts for
7 papers
Too Brittle To Touch: Comparing the Stability of Quantization and Distillation Towards Developing Lightweight Low-Resource MT Models
Harshita Diddee, Sandipan Dandapat, Monojit Choudhury +2
Leveraging shared learning through Massively Multilingual Models, state-of-the-art machine translation models are often able to adapt to the paucity of data for low-resource langua…
On the Calibration of Massively Multilingual Language Models
Kabir Ahuja, Sunayana Sitaram, Sandipan Dandapat +1
Massively Multilingual Language Models (MMLMs) have recently gained popularity due to their surprising effectiveness in cross-lingual transfer. While there has been much work in ev…
Multi Task Learning For Zero Shot Performance Prediction of Multilingual Models
Kabir Ahuja, Shanu Kumar, Sandipan Dandapat +1
Massively Multilingual Transformer based Language Models have been observed to be surprisingly effective on zero-shot transfer across languages, though the performance varies from…
Predicting the Performance of Multilingual NLP Models
Anirudh Srinivasan, Sunayana Sitaram, Tanuja Ganu +3
Recent advancements in NLP have given us models like mBERT and XLMR that can serve over 100 languages. The languages that these models are evaluated on, however, are very few in nu…
GLUECoS : An Evaluation Benchmark for Code-Switched NLP
Simran Khanuja, Sandipan Dandapat, Anirudh Srinivasan +2
Code-switching is the use of more than one language in the same conversation or utterance. Recently, multilingual contextual embedding models, trained on multiple monolingual corpo…
A New Dataset for Natural Language Inference from Code-mixed Conversations
Simran Khanuja, Sandipan Dandapat, Sunayana Sitaram +1
Natural Language Inference (NLI) is the task of inferring the logical relationship, typically entailment or contradiction, between a premise and hypothesis. Code-mixing is the use…