Guillaume Klein


2022

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Robust Translation of French Live Speech Transcripts
Elise Bertin-Lemée | Guillaume Klein | Josep Crego | Jean Senellart
Proceedings of the 15th Biennial Conference of the Association for Machine Translation in the Americas (Volume 2: Users and Providers Track and Government Track)

Despite a narrowed performance gap with direct approaches, cascade solutions, involving automatic speech recognition (ASR) and machine translation (MT) are still largely employed in speech translation (ST). Direct approaches employing a single model to translate the input speech signal suffer from the critical bottleneck of data scarcity. In addition, multiple industry applications display speech transcripts alongside translations, making cascade approaches more realistic and practical. In the context of cascaded simultaneous ST, we propose several solutions to adapt a neural MT network to take as input the transcripts output by an ASR system. Adaptation is achieved by enriching speech transcripts and MT data sets so that they more closely resemble each other, thereby improving the system robustness to error propagation and enhancing result legibility for humans. We address aspects such as sentence boundaries, capitalisation, punctuation, hesitations, repetitions, homophones, etc. while taking into account the low latency requirement of simultaneous ST systems.

2020

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The OpenNMT Neural Machine Translation Toolkit: 2020 Edition
Guillaume Klein | François Hernandez | Vincent Nguyen | Jean Senellart
Proceedings of the 14th Conference of the Association for Machine Translation in the Americas (Volume 1: Research Track)

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Efficient and High-Quality Neural Machine Translation with OpenNMT
Guillaume Klein | Dakun Zhang | Clément Chouteau | Josep Crego | Jean Senellart
Proceedings of the Fourth Workshop on Neural Generation and Translation

This paper describes the OpenNMT submissions to the WNGT 2020 efficiency shared task. We explore training and acceleration of Transformer models with various sizes that are trained in a teacher-student setup. We also present a custom and optimized C++ inference engine that enables fast CPU and GPU decoding with few dependencies. By combining additional optimizations and parallelization techniques, we create small, efficient, and high-quality neural machine translation models.

2018

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OpenNMT: Neural Machine Translation Toolkit
Guillaume Klein | Yoon Kim | Yuntian Deng | Vincent Nguyen | Jean Senellart | Alexander Rush
Proceedings of the 13th Conference of the Association for Machine Translation in the Americas (Volume 1: Research Track)

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OpenNMT System Description for WNMT 2018: 800 words/sec on a single-core CPU
Jean Senellart | Dakun Zhang | Bo Wang | Guillaume Klein | Jean-Pierre Ramatchandirin | Josep Crego | Alexander Rush
Proceedings of the 2nd Workshop on Neural Machine Translation and Generation

We present a system description of the OpenNMT Neural Machine Translation entry for the WNMT 2018 evaluation. In this work, we developed a heavily optimized NMT inference model targeting a high-performance CPU system. The final system uses a combination of four techniques, all of them lead to significant speed-ups in combination: (a) sequence distillation, (b) architecture modifications, (c) precomputation, particularly of vocabulary, and (d) CPU targeted quantization. This work achieves the fastest performance of the shared task, and led to the development of new features that have been integrated to OpenNMT and available to the community.

2017

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SYSTRAN Purely Neural MT Engines for WMT2017
Yongchao Deng | Jungi Kim | Guillaume Klein | Catherine Kobus | Natalia Segal | Christophe Servan | Bo Wang | Dakun Zhang | Josep Crego | Jean Senellart
Proceedings of the Second Conference on Machine Translation

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OpenNMT: Open-Source Toolkit for Neural Machine Translation
Guillaume Klein | Yoon Kim | Yuntian Deng | Jean Senellart | Alexander Rush
Proceedings of ACL 2017, System Demonstrations