The Case for Contextual Evaluation in Measuring LLM Translation Quality
At SlatorCon Zurich, Dr. Sheila Castilho emphasizes the significance of contextual evaluation in assessing large...
#BLEU
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Ghent University’s MATEO Project Improves Access to Machine Translation Evaluation
MATEO aims to open up machine translation evaluation, making it accessible to more stakeholders and...
What You Need to Know About Bias in Machine Translation
As machine translation gets better, the problem of bias — especially gender bias — remains...
Meta Tries Making Human Evaluation of Machine Translation More Consistent
Social media giant Meta proposes new metric to enable more consistency in human evaluation of...
Facebook (You Know, Meta) CTO Hypes WMT 2021 Win as ‘Truly Groundbreaking’
To be fair, this is the first-ever multilingual model to win the international machine translation...
Guess Which Language Pair Emits the Most Carbon in Machine Translation
Researchers tracked CO2 emissions from training German, French, and English machine translation engines, and found...
769 Machine Translation Papers Show Automatic Evaluation Worsening
Combing through papers released from 2010 to 2020, researchers identify overreliance on metric BLEU to...
New Machine Translation Metric Measures Correct and Consistent Term Use
Facebook, Naver, and George Mason University release open-sourced metric based on Covid-19 terms; hope to...
How to Improve Automatic Machine Translation Evaluation? Add Humans, Scientists Say
Scientists propose new system to standardize automatic evaluation of NLP systems by adding humans in...
Focusing on BLEU Can Bias Machine Translation Output
Though beam search can boost BLEU scores, it can also lead to high rates of...
Here Are the Top 10 Most Influential Research Papers on Neural Machine Translation
The most-cited neural machine translation research papers show how NMT came to dominate the field...