RESEARCH

NLP

Unsupervised Quality Estimation for Neural Machine Translation

August 31, 2020

Abstract

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. Existing approaches require large amounts of expert annotated data, computation and time for training. As an alternative, we devise an unsupervised approach to QE where no training or access to additional resources besides the MT system itself is required. Different from most of the current work that treats the MT system as a black box, we explore useful information that can be extracted from the MT system as a by product of translation. By employing methods for uncertainty quantification, we achieve very good correlation with human judgments of quality, rivalling state-of-the-art supervised QE models. To evaluate our approach we collect the first dataset that enables work on both blackbox and glass-box approaches to QE.

Download the Paper

AUTHORS

Written by

Marina Fomicheva

Shuo Sun

Lisa Yankovskaya

Frédéric Blain

Francisco Guzmán

Mark Fishel

Nikolaos Aletras

Vishrav Chaudhary

Lucia Specia

Publisher

Association for Computational Linguistics (ACL)

Related Publications

June 03, 2019

NLP

FAIRSEQ: A Fast, Extensible Toolkit for Sequence Modeling | Facebook AI Research

FAIRSEQ is an open-source sequence modeling toolkit that allows researchers and developers to train custom models for translation, summarization, language modeling, and other text generation tasks. The toolkit is based on PyTorch and supports…

Myle Ott, Sergey Edunov, Alexei Baevski, Angela Fan, Sam Gross, Nathan Ng, David Grangier, Michael Auli

June 03, 2019

June 02, 2019

NLP

Cooperative Learning of Disjoint Syntax and Semantics | Facebook AI Research

There has been considerable attention devoted to models that learn to jointly infer an expression’s syntactic structure and its semantics. Yet, Nangia and Bowman (2018) has recently shown that the current best systems fail to learn the correct…

Serhii Havrylov, Germán Kruszewski, Armand Joulin

June 02, 2019

October 30, 2018

NLP

Loss in Translation: Learning Bilingual Word Mapping with a Retrieval Criterion | Facebook AI Research

Continuous word representations learned separately on distinct languages can be aligned so that their words become comparable in a common space. Existing works typically solve a least-square regression problem to learn a rotation aligning a…

Armand Joulin, Piotr Bojanowski, Tomas Mikolov, Hervé Jégou, Edouard Grave

October 30, 2018

October 31, 2018

NLP

Understanding Back-Translation at Scale | Facebook AI Research

An effective method to improve neural machine translation with monolingual data is to augment the parallel training corpus with back-translations of target language sentences. This work broadens the understanding of back-translation and…

Sergey Edunov, Myle Ott, Michael Auli, David Grangier

October 31, 2018

Help Us Pioneer The Future of AI

We share our open source frameworks, tools, libraries, and models for everything from research exploration to large-scale production deployment.