Search Results for author: Frédéric Blain

Found 13 papers, 7 papers with code

Findings of the WMT 2021 Shared Task on Quality Estimation

no code implementations WMT (EMNLP) 2021 Lucia Specia, Frédéric Blain, Marina Fomicheva, Chrysoula Zerva, Zhenhao Li, Vishrav Chaudhary, André F. T. Martins

We report the results of the WMT 2021 shared task on Quality Estimation, where the challenge is to predict the quality of the output of neural machine translation systems at the word and sentence levels.

Machine Translation Translation

BERGAMOT-LATTE Submissions for the WMT20 Quality Estimation Shared Task

no code implementations WMT (EMNLP) 2020 Marina Fomicheva, Shuo Sun, Lisa Yankovskaya, Frédéric Blain, Vishrav Chaudhary, Mark Fishel, Francisco Guzmán, Lucia Specia

We explore (a) a black-box approach to QE based on pre-trained representations; and (b) glass-box approaches that leverage various indicators that can be extracted from the neural MT systems.

Findings of the WMT 2020 Shared Task on Quality Estimation

no code implementations WMT (EMNLP) 2020 Lucia Specia, Frédéric Blain, Marina Fomicheva, Erick Fonseca, Vishrav Chaudhary, Francisco Guzmán, André F. T. Martins

We report the results of the WMT20 shared task on Quality Estimation, where the challenge is to predict the quality of the output of neural machine translation systems at the word, sentence and document levels.

Machine Translation Translation

Tailoring Domain Adaptation for Machine Translation Quality Estimation

1 code implementation18 Apr 2023 Javad PourMostafa Roshan Sharami, Dimitar Shterionov, Frédéric Blain, Eva Vanmassenhove, Mirella De Sisto, Chris Emmery, Pieter Spronck

While quality estimation (QE) can play an important role in the translation process, its effectiveness relies on the availability and quality of training data.

Data Augmentation Domain Adaptation +3

Pushing the Right Buttons: Adversarial Evaluation of Quality Estimation

1 code implementation WMT (EMNLP) 2021 Diptesh Kanojia, Marina Fomicheva, Tharindu Ranasinghe, Frédéric Blain, Constantin Orăsan, Lucia Specia

However, this ability is yet to be tested in the current evaluation practices, where QE systems are assessed only in terms of their correlation with human judgements.

Machine Translation Translation

Knowledge Distillation for Quality Estimation

1 code implementation Findings (ACL) 2021 Amit Gajbhiye, Marina Fomicheva, Fernando Alva-Manchego, Frédéric Blain, Abiola Obamuyide, Nikolaos Aletras, Lucia Specia

Quality Estimation (QE) is the task of automatically predicting Machine Translation quality in the absence of reference translations, making it applicable in real-time settings, such as translating online social media conversations.

Data Augmentation Knowledge Distillation +2

Backtranslation Feedback Improves User Confidence in MT, Not Quality

1 code implementation NAACL 2021 Vilém Zouhar, Michal Novák, Matúš Žilinec, Ondřej Bojar, Mateo Obregón, Robin L. Hill, Frédéric Blain, Marina Fomicheva, Lucia Specia, Lisa Yankovskaya

Translating text into a language unknown to the text's author, dubbed outbound translation, is a modern need for which the user experience has significant room for improvement, beyond the basic machine translation facility.

Machine Translation Translation

Unsupervised Quality Estimation for Neural Machine Translation

3 code implementations21 May 2020 Marina Fomicheva, Shuo Sun, Lisa Yankovskaya, Frédéric Blain, Francisco Guzmán, Mark Fishel, Nikolaos Aletras, Vishrav Chaudhary, Lucia Specia

Quality Estimation (QE) is an important component in making Machine Translation (MT) useful in real-world applications, as it is aimed to inform the user on the quality of the MT output at test time.

Machine Translation Translation

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