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1
The Karlsruhe Institute of Technology Systems for the News Translation Task in WMT 2017
Waibel, Alexander; Ha, Thanh-Le; Sperber, Matthias. - : Association for Computational Linguistics, 2022
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2
Attention-Passing Models for Robust and Data-Efficient End-to-End Speech Translation
In: Transactions of the Association for Computational Linguistics, 7, 313–325 ; ISSN: 2307-387X (2022)
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3
KIT Lecture Translator: Multilingual Speech Translation with One-Shot Learning
Nguyen, Thai-Son; Zenkel, Thomas; Waibel, Alex. - : Association for Computational Linguistics, 2022
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4
Lecture Translator Speech translation framework for simultaneous lecture translation
Waibel, Alex; Nguyen, Thai-Son; Cho, Eunah. - : Association for Computational Linguistics, 2022
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5
Open Source Toolkit for Speech to Text Translation
In: The Prague Bulletin of Mathematical Linguistics, 111 (1), 125–135 ; ISSN: 0032-6585, 1804-0462 (2022)
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6
The Karlsruhe Institute of Technology Systems for the News Translation Task in WMT 2016
Cho, Eunah; Niehues, Jan; Allauzen, Alexandre. - : Association for Computational Linguistics, 2022
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7
Lightly Supervised Quality Estimation
Waibel, Alex; Niehues, Jan; Stüker, Sebastian. - : Association for Computational Linguistics, 2022
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8
Variational Neural Machine Translation with Normalizing Flows ...
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9
Self-Attentional Models for Lattice Inputs ...
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10
Attention-Passing Models for Robust and Data-Efficient End-to-End Speech Translation
In: Transactions of the Association for Computational Linguistics, Vol 7, Pp 313-325 (2019) (2019)
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11
Paraphrases as Foreign Languages in Multilingual Neural Machine Translation ...
Abstract: Paraphrases, the rewordings of the same semantic meaning, are useful for improving generalization and translation. However, prior works only explore paraphrases at the word or phrase level, not at the sentence or corpus level. Unlike previous works that only explore paraphrases at the word or phrase level, we use different translations of the whole training data that are consistent in structure as paraphrases at the corpus level. We train on parallel paraphrases in multiple languages from various sources. We treat paraphrases as foreign languages, tag source sentences with paraphrase labels, and train on parallel paraphrases in the style of multilingual Neural Machine Translation (NMT). Our multi-paraphrase NMT that trains only on two languages outperforms the multilingual baselines. Adding paraphrases improves the rare word translation and increases entropy and diversity in lexical choice. Adding the source paraphrases boosts performance better than adding the target ones. Combining both the source and the ...
Keyword: Computation and Language cs.CL; FOS Computer and information sciences
URL: https://dx.doi.org/10.48550/arxiv.1808.08438
https://arxiv.org/abs/1808.08438
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12
Open Source Toolkit for Speech to Text Translation
In: Prague Bulletin of Mathematical Linguistics , Vol 111, Iss 1, Pp 125-135 (2018) (2018)
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13
Comparison of Decoding Strategies for CTC Acoustic Models ...
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14
Neural Lattice-to-Sequence Models for Uncertain Inputs
Sperber, Matthias; Neubig, Graham; Waibel, Alex. - : Association for Computational Linguistics, 2017
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15
The Karlsruhe Institute of Technology Systems for the News Translation Task in WMT 2016
Ha, Thanh-Le [Verfasser]; Cho, Eunah [Verfasser]; Niehues, Jan [Verfasser]. - Aachen : Universitätsbibliothek der RWTH Aachen, 2016
DNB Subject Category Language
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