284 citations · 1.1k across the 20 of their papers we have counts for
5 papers · 1 filter
Human-Paraphrased References Improve Neural Machine Translation
Markus Freitag, George Foster, David Grangier +1
Automatic evaluation comparing candidate translations to human-generated paraphrases of reference translations has recently been proposed by Freitag et al. When used in place of or…
Complete Multilingual Neural Machine Translation
Markus Freitag, Orhan Firat
Multilingual Neural Machine Translation (MNMT) models are commonly trained on a joint set of bilingual corpora which is acutely English-centric (i.e. English either as the source o…
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…
KoBE: Knowledge-Based Machine Translation Evaluation
Zorik Gekhman, Roee Aharoni, Genady Beryozkin +2
We propose a simple and effective method for machine translation evaluation which does not require reference translations. Our approach is based on (1) grounding the entity mention…
BLEU might be Guilty but References are not Innocent
Markus Freitag, David Grangier, Isaac Caswell
The quality of automatic metrics for machine translation has been increasingly called into question, especially for high-quality systems. This paper demonstrates that, while choice…