35 citations · 76 across the 6 of their papers we have counts for
15 papers
Dialect-robust Evaluation of Generated Text
Jiao Sun, Thibault Sellam, Elizabeth Clark +6
Evaluation metrics that are not robust to dialect variation make it impossible to tell how well systems perform for many groups of users, and can even penalize systems for producin…
Towards the Next 1000 Languages in Multilingual Machine Translation: Exploring the Synergy Between Supervised and Self-Supervised Learning
Aditya Siddhant, Ankur Bapna, Orhan Firat +4
Achieving universal translation between all human language pairs is the holy-grail of machine translation (MT) research. While recent progress in massively multilingual MT is one s…
nmT5 -- Is parallel data still relevant for pre-training massively multilingual language models?
Mihir Kale, Aditya Siddhant, Noah Constant +3
Recently, mT5 - a massively multilingual version of T5 - leveraged a unified text-to-text format to attain state-of-the-art results on a wide variety of multilingual NLP tasks. In…
XTREME-R: Towards More Challenging and Nuanced Multilingual Evaluation
Sebastian Ruder, Noah Constant, Jan Botha +8
Machine learning has brought striking advances in multilingual natural language processing capabilities over the past year. For example, the latest techniques have improved the sta…
Distilling Large Language Models into Tiny and Effective Students using pQRNN
Prabhu Kaliamoorthi, Aditya Siddhant, Edward Li +1
Large pre-trained multilingual models like mBERT, XLM-R achieve state of the art results on language understanding tasks. However, they are not well suited for latency critical app…
mT5: A massively multilingual pre-trained text-to-text transformer
Linting Xue, Noah Constant, Adam Roberts +5
The recent "Text-to-Text Transfer Transformer" (T5) leveraged a unified text-to-text format and scale to attain state-of-the-art results on a wide variety of English-language NLP t…