25 citations · 223 across the 48 of their papers we have counts for
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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…
Generating Intermediate Steps for NLI with Next-Step Supervision
Deepanway Ghosal, Somak Aditya, Monojit Choudhury
The Natural Language Inference (NLI) task often requires reasoning over multiple steps to reach the conclusion. While the necessity of generating such intermediate steps (instead o…
"Diversity and Uncertainty in Moderation" are the Key to Data Selection for Multilingual Few-shot Transfer
Shanu Kumar, Sandipan Dandapat, Monojit Choudhury
Few-shot transfer often shows substantial gain over zero-shot transfer~\cite{lauscher2020zero}, which is a practically useful trade-off between fully supervised and unsupervised le…
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…
Beyond Static Models and Test Sets: Benchmarking the Potential of Pre-trained Models Across Tasks and Languages
Kabir Ahuja, Sandipan Dandapat, Sunayana Sitaram +1
Although recent Massively Multilingual Language Models (MMLMs) like mBERT and XLMR support around 100 languages, most existing multilingual NLP benchmarks provide evaluation data i…