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Current research into spoken language translation (SLT),or speech-to-text translation, is often hampered by the lack of specific data resources for this task, as currently available SLT datasets are restricted to a limited set of language pairs.
“Bleu: a Method for Automatic Evaluation of Machine Translation,”
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu, · 2002
Earlier work this paper cites.
“SRILM – an extensible language modeling toolkit,”
A. Stolcke, · 2002
Earlier work this paper cites.
“Evaluating machine translation output with automatic sentence segmentation,”
Evgeny Matusov, Gregor Leusch, Oliver Bender, and Hermann Ney, · 2005
Earlier work this paper cites.
“Moses: Open source toolkit for statistical machine translation,”
Philipp Koehn et al., · 2007
Earlier work this paper cites.
“Improved unsupervised sentence alignment for symmetrical and asymmetrical parallel corpora,”
Fabienne Braune and Alexander M. Fraser, · 2010
Earlier work this paper cites.
“Parallel data, tools and interfaces in OPUS,”
Jörg Tiedemann, · 2012
Earlier work this paper cites.
“Spoken language translation using automatically transcribed text in training,”
Stephan Peitz, Simon Wiesler, Markus Nußbaum-Thom, and Hermann Ney, · 2012
Earlier work this paper cites.
“Improved speech-to-text translation with the Fisher and Callhome Spanish–English speech translation corpus,”
Matt Post, Gaurav Kumar, Adam Lopez, Damianos Karakos, Chris Callison-Burch, and Sanjeev Khudanpur, · 2013
Earlier work this paper cites.
“An open-source state-of-the-art toolbox for broadcast news diarization,”
Mickael Rouvier, Grégor Dupuy, Paul Gay, Elie el Khoury, Téva Merlin, and Sylvain Meignier, · 2013
Earlier work this paper cites.
“A simple, fast, and effective reparameterization of IBM model 2,”
Chris Dyer, Victor Chahuneau, and Noah A. Smith, · 2013
Cited alongside, same era.
“The Translectures-UPV Toolkit,”
Miguel A. del Agua, Adrià Giménez, Nicolás Serrano, Jesús Andrés-Ferrer, Jorge Civera, Alberto Sanchís, and Alfons Juan, · 2014
Cited alongside, same era.
“CUED-RNNLM — An open-source toolkit for efficient training and evaluation of recurrent neural network language models,”
Xi Chen, Xin Liu, Y. Qian, Mark J. F. Gales, and Philip C. Woodland, · 2016
Cited alongside, same era.
“Neural machine translation of rare words with subword units,”
Rico Sennrich, Barry Haddow, and Alexandra Birch, · 2016
Cited alongside, same era.
“Toward robust neural machine translation for noisy input sequences,”
Matthias Sperber, Jan Niehues, and Alex Waibel, · 2017
Cited alongside, same era.
“NMT-Based Segmentation and Punctuation Insertion for Real-Time Spoken Language Translation,”
“Attention is all you need,”
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin, · 2017
Later among the works it cites.
“Neural Speech Translation at AppTek,”
E. Matusov, P. Wilken, P. Bahar, J. Schamper, P. Golik, A. Zeyer, J.A. Silvestre-Cerdà, A. Martínez-Villaronga, H. Pesch, and J. Peter, · 2018
Later among the works it cites.
“The IWSLT 2018 Evaluation Campaign,”
Jan Niehues, Roldano Cattoni, Sebastia Stüker, Mauro Cettolo, Marco Turchi, and Marcello Federico, · 2018
Later among the works it cites.
“Attention-passing models for robust and data-efficient end-to-end speech translation,”
Matthias Sperber, Graham Neubig, Jan Niehues, and Alex Waibel, · 2019
Closest in time.
“Exploring phoneme-level speech representations for end-to-end speech translation,”
Elizabeth Salesky, Matthias Sperber, and Alan W Black, · 2019
Closest in time.
“Direct Speech-to-Speech Translation with a Sequence-to-Sequence Model,”
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Eunah Cho, Jan Niehues, and Alex Waibel, · 2017
Cited alongside, same era.
“Sequence-to-sequence models can directly translate foreign speech,”
Ron J. Weiss, Jan Chorowski, Navdeep Jaitly, Yonghui Wu, and Zhifeng Chen, · 2017
Cited alongside, same era.
“The debates of the European Parliament as linked open data,”
Astrid Van Aggelen, Laura Hollink, Max Kemman, Martijn Kleppe, and Henri Beunders, · 2017
Cited alongside, same era.
“A call for clarity in reporting BLEU scores,”
Matt Post,
Cited in the paper.
“Tensorflow,” https://www.tensorflow.org/
Cited in the paper.
“The RNNLM Toolkit,” http://www.fit.vutbr.cz/~imikolov/rnnlm/
Cited in the paper.
Ye Jia, Ron J. Weiss, Fadi Biadsy, Wolfgang Macherey, Melvin Johnson, Zhifeng Chen, and Yonghui Wu, · 2019
Closest in time.
“MuST-C: a Multilingual Speech Translation Corpus,”
Mattia Antonino Di Gangi, Roldano Cattoni, Luisa Bentivogli, Matteo Negri, and Marco Turchi, · 2019
Closest in time.
“Mass: A large and clean multilingual corpus of sentence-aligned spoken utterances extracted from the bible,”
Marcely Zanon Boito, William N. Havard, Mahault Garnerin, ´Eric Le Ferrand, and Laurent Besacier, · 2020
Closest in time.