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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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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)
Abstract: The 'human-machine era' is coming soon: a time when technology is integrated with our senses, not confined to mobile devices. The hardware will move from our hands into our eyes and ears. Intelligent eyewear and earwear will be able to translate another person's words, and make it look and sound like they were talking to you in your language. Technology will mediate what we see, hear and say, in real time. In addition, we will be having increasingly complex conversations with smart devices. This is not science fiction or marketing hype. These devices are currently in prototype, set for widespread consumer adoption in the coming years. All this will disrupt and transform our use and understanding of language use. Are we ready?A new EU 'COST Action' (https://cost.eu) research network 'Language in the Human-Machine Era' (LITHME), with members from 52 countries, explores how such technological advances are likely to change our everyday communication, and ultimately language itself. As a first major collaborative effort, LITHME has published an open access report 'The Dawn of the Human-Machine Era: A Forecast of New and Emerging Language Technologies': https://doi.org/10.17011/jyx/reports/20210518/1.Accessible to a wide audience, the report brings together insights from specialists in the fields of language technology and linguistic research.The forecast report was authored by 52 researchers, and edited by LITHME's Chair Dave Sayers (University of Jyväskylä, Finland), Vice-Chair Sviatlana Höhn (University of Luxembourg), and the Chair of LITHME's Computational Linguistics working group Rui Sousa Silva (University of Porto, Portugal). It describes the current state and probable futures of various language technologies – for written, spoken, haptic and signed modalities of language.The publication is intended to be both authoritative and accessible, aimed at language and technology professionals but also policymakers and the wider public. It describes how a range of new technologies will soon transform the way we use language, while discussing the software powering these advances behind the scenes, as well as consumer devices like Augmented Reality eyepieces and immersive Virtual Reality spaces. The report also shines a light on critical issues such as inequality of access to technologies, privacy and security, and new forms of deception and crime.It is a result of unique collaboration, as LITHME brings together people from different directions in language research who would not otherwise meet or collaborate. LITHME has eight thematic working groups; and members from each working group have contributed to the report.
Keyword: [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]; [INFO.INFO-TT]Computer Science [cs]/Document and Text Processing
URL: https://hal.archives-ouvertes.fr/hal-03230287
https://hal.archives-ouvertes.fr/hal-03230287/document
https://hal.archives-ouvertes.fr/hal-03230287/file/The%20Forecast%20report%202021%20%28May%2019%2008.28%29.pdf
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6
Self-Learning for Zero Shot Neural Machine Translation ...
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Gender Bias in Machine Translation ...
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How to Split: the Effect of Word Segmentation on Gender Bias in Speech Translation ...
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The Multilingual TEDx Corpus for Speech Recognition and Translation ...
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Simultaneous Speech Translation for Live Subtitling: from Delay to Display ...
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How to Split: the Effect of Word Segmentation on Gender Bias in Speech Translation ...
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Speechformer: Reducing Information Loss in Direct Speech Translation ...
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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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Speechformer: Reducing Information Loss in Direct Speech Translation ...
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Gender Bias in Machine Translation ...
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Cascade versus Direct Speech Translation: Do the Differences Still Make a Difference? ...
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CTC-based Compression for Direct Speech Translation ...
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Between Flexibility and Consistency: Joint Generation of Captions and Subtitles ...
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The Dawn of the Human-Machine Era: A forecast of new and emerging language technologies
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