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1
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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3
Tutorial: End-to-End Speech Translation
Negri, Matteo; Salesky, Elizabeth; Turchi, Marco. - : Association for Computational Linguistics, 2022
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4
The IWSLT 2018 Evaluation Campaign
Turchi, Marco; Federico, Marcello; Jan, Niehues. - : Association for Computational Linguistics, 2022
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5
The Dawn of the Human-Machine Era: A forecast of new and emerging language technologies.
In: https://hal.archives-ouvertes.fr/hal-03230287 ; 2021 (2021)
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6
Self-Learning for Zero Shot Neural Machine Translation ...
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7
Gender Bias in Machine Translation ...
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8
How to Split: the Effect of Word Segmentation on Gender Bias in Speech Translation ...
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9
The Multilingual TEDx Corpus for Speech Recognition and Translation ...
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10
Simultaneous Speech Translation for Live Subtitling: from Delay to Display ...
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11
How to Split: the Effect of Word Segmentation on Gender Bias in Speech Translation ...
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12
Speechformer: Reducing Information Loss in Direct Speech Translation ...
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13
Tutorial: End-to-End Speech Translation ...
Niehues, Jan; Salesky, Elizabeth; Turchi, Marco. - : Association for Computational Linguistics, 2021
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14
Is “moby dick” a Whale or a Bird? Named Entities and Terminology in Speech Translation ...
Abstract: Anthology paper link: https://aclanthology.org/2021.emnlp-main.128/ Abstract: Automatic translation systems are known to struggle with rare words. Among these, named entities (NEs) and domain-specific terms are crucial, since errors in their translation can lead to severe meaning distortions. Despite their importance, previous speech translation (ST) studies have neglected them, also due to the dearth of publicly available resources tailored to their specific evaluation. To fill this gap, we i) present the first systematic analysis of the behavior of state-of-the-art ST systems in translating NEs and terminology, and ii) release NEuRoparl-ST, a novel benchmark built from European Parliament speeches annotated with NEs and terminology. Our experiments on the three language directions covered by our benchmark (en->es/fr/it) show that ST systems correctly translate 75-80% of terms and 65-70% of NEs, with very low performance (37-40%) on person names. ...
Keyword: Computational Linguistics; Machine Learning; Machine Learning and Data Mining; Machine translation; Named Entity Recognition; Natural Language Processing
URL: https://underline.io/lecture/37668-is-moby-dick-a-whale-or-a-birdquestion-named-entities-and-terminology-in-speech-translation
https://dx.doi.org/10.48448/tnqx-z428
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15
Speechformer: Reducing Information Loss in Direct Speech Translation ...
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16
Gender Bias in Machine Translation ...
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17
Cascade versus Direct Speech Translation: Do the Differences Still Make a Difference? ...
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18
CTC-based Compression for Direct Speech Translation ...
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19
Between Flexibility and Consistency: Joint Generation of Captions and Subtitles ...
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20
The Dawn of the Human-Machine Era: A forecast of new and emerging language technologies
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