Search Results for author: Luisa Bentivogli

Found 33 papers, 5 papers with code

On the Dynamics of Gender Learning in Speech Translation

no code implementations NAACL (GeBNLP) 2022 Beatrice Savoldi, Marco Gaido, Luisa Bentivogli, Matteo Negri, Marco Turchi

In this work, we contribute to such a line of inquiry by exploring the emergence of gender bias in Speech Translation (ST).


Towards a methodology for evaluating automatic subtitling

no code implementations EAMT 2022 Alina Karakanta, Luisa Bentivogli, Mauro Cettolo, Matteo Negri, Marco Turchi

In response to the increasing interest towards automatic subtitling, this EAMT-funded project aimed at collecting subtitle post-editing data in a real use case scenario where professional subtitlers edit automatically generated subtitles.

Post-editing in Automatic Subtitling: A Subtitlers’ perspective

1 code implementation EAMT 2022 Alina Karakanta, Luisa Bentivogli, Mauro Cettolo, Matteo Negri, Marco Turchi

Subtitling tools are recently being adapted for post-editing by providing automatically generated subtitles, and featuring not only machine translation, but also automatic segmentation and synchronisation.

Machine Translation Translation

Findings of the IWSLT 2022 Evaluation Campaign

no code implementations IWSLT (ACL) 2022 Antonios Anastasopoulos, Loïc Barrault, Luisa Bentivogli, Marcely Zanon Boito, Ondřej Bojar, Roldano Cattoni, Anna Currey, Georgiana Dinu, Kevin Duh, Maha Elbayad, Clara Emmanuel, Yannick Estève, Marcello Federico, Christian Federmann, Souhir Gahbiche, Hongyu Gong, Roman Grundkiewicz, Barry Haddow, Benjamin Hsu, Dávid Javorský, Vĕra Kloudová, Surafel Lakew, Xutai Ma, Prashant Mathur, Paul McNamee, Kenton Murray, Maria Nǎdejde, Satoshi Nakamura, Matteo Negri, Jan Niehues, Xing Niu, John Ortega, Juan Pino, Elizabeth Salesky, Jiatong Shi, Matthias Sperber, Sebastian Stüker, Katsuhito Sudoh, Marco Turchi, Yogesh Virkar, Alexander Waibel, Changhan Wang, Shinji Watanabe

The evaluation campaign of the 19th International Conference on Spoken Language Translation featured eight shared tasks: (i) Simultaneous speech translation, (ii) Offline speech translation, (iii) Speech to speech translation, (iv) Low-resource speech translation, (v) Multilingual speech translation, (vi) Dialect speech translation, (vii) Formality control for speech translation, (viii) Isometric speech translation.

Speech-to-Speech Translation Translation

CEF Data Marketplace: Powering a Long-term Supply of Language Data

no code implementations EAMT 2020 Amir Kamran, Dace Dzeguze, Jaap van der Meer, Milica Panic, Alessandro Cattelan, Daniele Patrioli, Luisa Bentivogli, Marco Turchi

We describe the CEF Data Marketplace project, which focuses on the development of a trading platform of translation data for language professionals: translators, machine translation (MT) developers, language service providers (LSPs), translation buyers and government bodies.

Machine Translation Translation

Machine Translation Human Evaluation: an investigation of evaluation based on Post-Editing and its relation with Direct Assessment

no code implementations IWSLT (EMNLP) 2018 Luisa Bentivogli, Mauro Cettolo, Marcello Federico, Christian Federmann

In this paper we present an analysis of the two most prominent methodologies used for the human evaluation of MT quality, namely evaluation based on Post-Editing (PE) and evaluation based on Direct Assessment (DA).

Machine Translation

Under the Morphosyntactic Lens: A Multifaceted Evaluation of Gender Bias in Speech Translation

1 code implementation ACL 2022 Beatrice Savoldi, Marco Gaido, Luisa Bentivogli, Matteo Negri, Marco Turchi

Gender bias is largely recognized as a problematic phenomenon affecting language technologies, with recent studies underscoring that it might surface differently across languages.

POS Translation

Cascade versus Direct Speech Translation: Do the Differences Still Make a Difference?

no code implementations ACL 2021 Luisa Bentivogli, Mauro Cettolo, Marco Gaido, Alina Karakanta, Alberto Martinelli, Matteo Negri, Marco Turchi

Five years after the first published proofs of concept, direct approaches to speech translation (ST) are now competing with traditional cascade solutions.


How to Split: the Effect of Word Segmentation on Gender Bias in Speech Translation

1 code implementation Findings (ACL) 2021 Marco Gaido, Beatrice Savoldi, Luisa Bentivogli, Matteo Negri, Marco Turchi

In light of this finding, we propose a combined approach that preserves BPE overall translation quality, while leveraging the higher ability of character-based segmentation to properly translate gender.


Gender Bias in Machine Translation

1 code implementation13 Apr 2021 Beatrice Savoldi, Marco Gaido, Luisa Bentivogli, Matteo Negri, Marco Turchi

Machine translation (MT) technology has facilitated our daily tasks by providing accessible shortcuts for gathering, elaborating and communicating information.

Machine Translation Translation

Breeding Gender-aware Direct Speech Translation Systems

no code implementations COLING 2020 Marco Gaido, Beatrice Savoldi, Luisa Bentivogli, Matteo Negri, Marco Turchi

In particular, by translating speech audio data without intermediate transcription, direct ST models are able to leverage and preserve essential information present in the input (e. g. speaker's vocal characteristics) that is otherwise lost in the cascade framework.

Machine Translation Translation

Machine Translation for Machines: the Sentiment Classification Use Case

no code implementations IJCNLP 2019 Amirhossein Tebbifakhr, Luisa Bentivogli, Matteo Negri, Marco Turchi

Towards this objective, we present a reinforcement learning technique based on a new candidate sampling strategy, which exploits the results obtained on the downstream task as weak feedback.

Classification General Classification +7

Neural versus Phrase-Based Machine Translation Quality: a Case Study

no code implementations EMNLP 2016 Luisa Bentivogli, Arianna Bisazza, Mauro Cettolo, Marcello Federico

Within the field of Statistical Machine Translation (SMT), the neural approach (NMT) has recently emerged as the first technology able to challenge the long-standing dominance of phrase-based approaches (PBMT).

Machine Translation NMT +1

WAGS: A Beautiful English-Italian Benchmark Supporting Word Alignment Evaluation on Rare Words

no code implementations LREC 2016 Luisa Bentivogli, Mauro Cettolo, M. Amin Farajian, Marcello Federico

This paper presents WAGS (Word Alignment Gold Standard), a novel benchmark which allows extensive evaluation of WA tools on out-of-vocabulary (OOV) and rare words.

Word Alignment

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