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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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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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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 ...
Abstract: Read paper: https://www.aclanthology.org/2021.findings-acl.313 Abstract: Having recognized gender bias as a major issue affecting current translation technologies, researchers have primarily attempted to mitigate it by working on the data front. However, whether algorithmic aspects concur to exacerbate unwanted outputs remains so far under-investigated. In this work, we bring the analysis on gender bias in automatic translation onto a seemingly neutral yet critical component: word segmentation. Can segmenting methods influence the ability to translate gender? Do certain segmentation approaches penalize the representation of feminine linguistic markings? We address these questions by comparing 5 existing segmentation strategies on the target side of speech translation systems. Our results on two language pairs (English-Italian/French) show that state-of-the-art subword splitting (BPE) comes at the cost of higher gender bias. In light of this finding, we propose a combined approach that preserves BPE overall ...
Keyword: Computational Linguistics; Condensed Matter Physics; Deep Learning; Electromagnetism; FOS Physical sciences; Gender Studies; Information and Knowledge Engineering; Neural Network; Semantics
URL: https://underline.io/lecture/26404-how-to-split-the-effect-of-word-segmentation-on-gender-bias-in-speech-translation
https://dx.doi.org/10.48448/ydta-r725
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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 ...
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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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