139 citations · 635 across the 23 of their papers we have counts for
4 papers · 1 filter
Learning to Evaluate Translation Beyond English: BLEURT Submissions to the WMT Metrics 2020 Shared Task
Thibault Sellam, Amy Pu, Hyung Won Chung +5
The quality of machine translation systems has dramatically improved over the last decade, and as a result, evaluation has become an increasingly challenging problem. This paper de…
BLEURT: Learning Robust Metrics for Text Generation
Thibault Sellam, Dipanjan Das, Ankur P. Parikh
Text generation has made significant advances in the last few years. Yet, evaluation metrics have lagged behind, as the most popular choices (e.g., BLEU and ROUGE) may correlate po…
Syntactic Data Augmentation Increases Robustness to Inference Heuristics
Junghyun Min, R. Thomas McCoy, Dipanjan Das +2
Pretrained neural models such as BERT, when fine-tuned to perform natural language inference (NLI), often show high accuracy on standard datasets, but display a surprising lack of…
ToTTo: A Controlled Table-To-Text Generation Dataset
Ankur P. Parikh, Xuezhi Wang, Sebastian Gehrmann +4
We present ToTTo, an open-domain English table-to-text dataset with over 120,000 training examples that proposes a controlled generation task: given a Wikipedia table and a set of…