activity
20182022
most citedThe GEM Benchmark: Natural Language Generation, its Evaluation and Metrics

52 citations · 79 across the 10 of their papers we have counts for

collaborators

13 papers

cs.CL20222 cited

Mining Duplicate Questions of Stack Overflow

Mihir Kale, Anirudha Rayasam, Radhika Parik +1

There has a been a significant rise in the use of Community Question Answering sites (CQAs) over the last decade owing primarily to their ability to leverage the wisdom of the crow…

cs.CL20221 cited

XTREME-S: Evaluating Cross-lingual Speech Representations

Alexis Conneau, Ankur Bapna, Yu Zhang +16

We introduce XTREME-S, a new benchmark to evaluate universal cross-lingual speech representations in many languages. XTREME-S covers four task families: speech recognition, classif…

cs.CL2021

Using Machine Translation to Localize Task Oriented NLG Output

Scott Roy, Cliff Brunk, Kyu-Young Kim +6

One of the challenges in a task oriented natural language application like the Google Assistant, Siri, or Alexa is to localize the output to many languages. This paper explores doi…

cs.CL202112 cited

Automatic Construction of Evaluation Suites for Natural Language Generation Datasets

Simon Mille, Kaustubh D. Dhole, Saad Mahamood +5

Machine learning approaches applied to NLP are often evaluated by summarizing their performance in a single number, for example accuracy. Since most test sets are constructed as an…

cs.CL20211 cited

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…

cs.CL202152 cited

The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics

Sebastian Gehrmann, Tosin Adewumi, Karmanya Aggarwal +53

We introduce GEM, a living benchmark for natural language Generation (NLG), its Evaluation, and Metrics. Measuring progress in NLG relies on a constantly evolving ecosystem of auto…