Search Results for author: Wolfgang Macherey

Found 17 papers, 7 papers with code

Self-supervised and Supervised Joint Training for Resource-rich Machine Translation

no code implementations8 Jun 2021 Yong Cheng, Wei Wang, Lu Jiang, Wolfgang Macherey

Self-supervised pre-training of text representations has been successfully applied to low-resource Neural Machine Translation (NMT).

Low-Resource Neural Machine Translation Translation

Experts, Errors, and Context: A Large-Scale Study of Human Evaluation for Machine Translation

3 code implementations29 Apr 2021 Markus Freitag, George Foster, David Grangier, Viresh Ratnakar, Qijun Tan, Wolfgang Macherey

Human evaluation of modern high-quality machine translation systems is a difficult problem, and there is increasing evidence that inadequate evaluation procedures can lead to erroneous conclusions.

Machine Translation Translation

KoBE: Knowledge-Based Machine Translation Evaluation

1 code implementation Findings of the Association for Computational Linguistics 2020 Zorik Gekhman, Roee Aharoni, Genady Beryozkin, Markus Freitag, Wolfgang Macherey

Our approach achieves the highest correlation with human judgements on 9 out of the 18 language pairs from the WMT19 benchmark for evaluation without references, which is the largest number of wins for a single evaluation method on this task.

Machine Translation Translation

Re-translation versus Streaming for Simultaneous Translation

no code implementations WS 2020 Naveen Arivazhagan, Colin Cherry, Wolfgang Macherey, George Foster

There has been great progress in improving streaming machine translation, a simultaneous paradigm where the system appends to a growing hypothesis as more source content becomes available.

Data Augmentation Machine Translation +1

Re-Translation Strategies For Long Form, Simultaneous, Spoken Language Translation

1 code implementation6 Dec 2019 Naveen Arivazhagan, Colin Cherry, Te I, Wolfgang Macherey, Pallavi Baljekar, George Foster

As this scenario allows for revisions to our incremental translations, we adopt a re-translation approach to simultaneous translation, where the source is repeatedly translated from scratch as it grows.

Machine Translation Speech Recognition +1

Monotonic Infinite Lookback Attention for Simultaneous Machine Translation

no code implementations ACL 2019 Naveen Arivazhagan, Colin Cherry, Wolfgang Macherey, Chung-Cheng Chiu, Semih Yavuz, Ruoming Pang, Wei Li, Colin Raffel

Simultaneous machine translation begins to translate each source sentence before the source speaker is finished speaking, with applications to live and streaming scenarios.

Machine Translation Translation

Robust Neural Machine Translation with Doubly Adversarial Inputs

1 code implementation ACL 2019 Yong Cheng, Lu Jiang, Wolfgang Macherey

Neural machine translation (NMT) often suffers from the vulnerability to noisy perturbations in the input.

Machine Translation Translation

Direct speech-to-speech translation with a sequence-to-sequence model

no code implementations12 Apr 2019 Ye Jia, Ron J. Weiss, Fadi Biadsy, Wolfgang Macherey, Melvin Johnson, Zhifeng Chen, Yonghui Wu

We present an attention-based sequence-to-sequence neural network which can directly translate speech from one language into speech in another language, without relying on an intermediate text representation.

Speech Synthesis Speech-to-Speech Translation +3

The Missing Ingredient in Zero-Shot Neural Machine Translation

no code implementations17 Mar 2019 Naveen Arivazhagan, Ankur Bapna, Orhan Firat, Roee Aharoni, Melvin Johnson, Wolfgang Macherey

Multilingual Neural Machine Translation (NMT) models are capable of translating between multiple source and target languages.

Machine Translation Translation

Lingvo: a Modular and Scalable Framework for Sequence-to-Sequence Modeling

3 code implementations21 Feb 2019 Jonathan Shen, Patrick Nguyen, Yonghui Wu, Zhifeng Chen, Mia X. Chen, Ye Jia, Anjuli Kannan, Tara Sainath, Yuan Cao, Chung-Cheng Chiu, Yanzhang He, Jan Chorowski, Smit Hinsu, Stella Laurenzo, James Qin, Orhan Firat, Wolfgang Macherey, Suyog Gupta, Ankur Bapna, Shuyuan Zhang, Ruoming Pang, Ron J. Weiss, Rohit Prabhavalkar, Qiao Liang, Benoit Jacob, Bowen Liang, HyoukJoong Lee, Ciprian Chelba, Sébastien Jean, Bo Li, Melvin Johnson, Rohan Anil, Rajat Tibrewal, Xiaobing Liu, Akiko Eriguchi, Navdeep Jaitly, Naveen Ari, Colin Cherry, Parisa Haghani, Otavio Good, Youlong Cheng, Raziel Alvarez, Isaac Caswell, Wei-Ning Hsu, Zongheng Yang, Kuan-Chieh Wang, Ekaterina Gonina, Katrin Tomanek, Ben Vanik, Zelin Wu, Llion Jones, Mike Schuster, Yanping Huang, Dehao Chen, Kazuki Irie, George Foster, John Richardson, Klaus Macherey, Antoine Bruguier, Heiga Zen, Colin Raffel, Shankar Kumar, Kanishka Rao, David Rybach, Matthew Murray, Vijayaditya Peddinti, Maxim Krikun, Michiel A. U. Bacchiani, Thomas B. Jablin, Rob Suderman, Ian Williams, Benjamin Lee, Deepti Bhatia, Justin Carlson, Semih Yavuz, Yu Zhang, Ian McGraw, Max Galkin, Qi Ge, Golan Pundak, Chad Whipkey, Todd Wang, Uri Alon, Dmitry Lepikhin, Ye Tian, Sara Sabour, William Chan, Shubham Toshniwal, Baohua Liao, Michael Nirschl, Pat Rondon

Lingvo is a Tensorflow framework offering a complete solution for collaborative deep learning research, with a particular focus towards sequence-to-sequence models.

Sequence-To-Sequence Speech Recognition

Leveraging Weakly Supervised Data to Improve End-to-End Speech-to-Text Translation

no code implementations5 Nov 2018 Ye Jia, Melvin Johnson, Wolfgang Macherey, Ron J. Weiss, Yuan Cao, Chung-Cheng Chiu, Naveen Ari, Stella Laurenzo, Yonghui Wu

In this paper, we demonstrate that using pre-trained MT or text-to-speech (TTS) synthesis models to convert weakly supervised data into speech-to-translation pairs for ST training can be more effective than multi-task learning.

Machine Translation Multi-Task Learning +3

Zero-Shot Cross-lingual Classification Using Multilingual Neural Machine Translation

no code implementations12 Sep 2018 Akiko Eriguchi, Melvin Johnson, Orhan Firat, Hideto Kazawa, Wolfgang Macherey

However, little attention has been paid to leveraging representations learned by a multilingual NMT system to enable zero-shot multilinguality in other NLP tasks.

Cross-Lingual Transfer General Classification +4

Revisiting Character-Based Neural Machine Translation with Capacity and Compression

no code implementations EMNLP 2018 Colin Cherry, George Foster, Ankur Bapna, Orhan Firat, Wolfgang Macherey

Translating characters instead of words or word-fragments has the potential to simplify the processing pipeline for neural machine translation (NMT), and improve results by eliminating hyper-parameters and manual feature engineering.

Feature Engineering Machine Translation +1

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