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Under the Morphosyntactic Lens: A Multifaceted Evaluation of Gender Bias in Speech Translation ...
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Findings of the IWSLT 2020 Evaluation campaign
Niehues, Jan; Federico, Marcello; Ma, Xutai. - : Association for Computational Linguistics, 2022
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Tutorial: End-to-End Speech Translation
Negri, Matteo; Salesky, Elizabeth; Turchi, Marco. - : Association for Computational Linguistics, 2022
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Self-Learning for Zero Shot Neural Machine Translation ...
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5
Gender Bias in Machine Translation ...
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6
How to Split: the Effect of Word Segmentation on Gender Bias in Speech Translation ...
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7
The Multilingual TEDx Corpus for Speech Recognition and Translation ...
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8
Simultaneous Speech Translation for Live Subtitling: from Delay to Display ...
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9
How to Split: the Effect of Word Segmentation on Gender Bias in Speech Translation ...
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10
Speechformer: Reducing Information Loss in Direct Speech Translation ...
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11
Tutorial: End-to-End Speech Translation ...
Niehues, Jan; Salesky, Elizabeth; Turchi, Marco. - : Association for Computational Linguistics, 2021
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12
Is “moby dick” a Whale or a Bird? Named Entities and Terminology in Speech Translation ...
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13
Speechformer: Reducing Information Loss in Direct Speech Translation ...
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14
Gender Bias in Machine Translation ...
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15
Cascade versus Direct Speech Translation: Do the Differences Still Make a Difference? ...
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16
CTC-based Compression for Direct Speech Translation ...
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17
Between Flexibility and Consistency: Joint Generation of Captions and Subtitles ...
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MuST-Cinema: a Speech-to-Subtitles corpus ...
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19
Low Resource Neural Machine Translation: A Benchmark for Five African Languages ...
Abstract: Recent advents in Neural Machine Translation (NMT) have shown improvements in low-resource language (LRL) translation tasks. In this work, we benchmark NMT between English and five African LRL pairs (Swahili, Amharic, Tigrigna, Oromo, Somali [SATOS]). We collected the available resources on the SATOS languages to evaluate the current state of NMT for LRLs. Our evaluation, comparing a baseline single language pair NMT model against semi-supervised learning, transfer learning, and multilingual modeling, shows significant performance improvements both in the En-LRL and LRL-En directions. In terms of averaged BLEU score, the multilingual approach shows the largest gains, up to +5 points, in six out of ten translation directions. To demonstrate the generalization capability of each model, we also report results on multi-domain test sets. We release the standardized experimental data and the test sets for future works addressing the challenges of NMT in under-resourced settings, in particular for the SATOS ... : Accepted for AfricaNLP workshop at ICLR 2020 ...
Keyword: Computation and Language cs.CL; FOS Computer and information sciences
URL: https://dx.doi.org/10.48550/arxiv.2003.14402
https://arxiv.org/abs/2003.14402
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Findings of the IWSLT 2020 Evaluation campaign ...
Ansari, Ebrahim; Axelrod, Amittai; Bach, Nguyen. - : Association for Computational Linguistics, 2020
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