no code implementations • EMNLP 2021 • Marco Gaido, Susana Rodríguez, Matteo Negri, Luisa Bentivogli, Marco Turchi
Automatic translation systems are known to struggle with rare words.
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).
no code implementations • EAMT 2022 • Luisa Bentivogli, Mauro Cettolo, Marco Gaido, Alina Karakanta, Matteo Negri, Marco Turchi
This project aimed at extending the test sets of the MuST-C speech translation (ST) corpus with new reference translations.
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.
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.
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.
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.
no code implementations • IWSLT 2017 • Mauro Cettolo, Marcello Federico, Luisa Bentivogli, Jan Niehues, Sebastian Stüker, Katsuhito Sudoh, Koichiro Yoshino, Christian Federmann
The IWSLT 2017 evaluation campaign has organised three tasks.
no code implementations • IWSLT 2016 • Mauro Cettolo, Jan Niehues, Sebastian Stüker, Luisa Bentivogli, Rolando Cattoni, Marcello Federico
The IWSLT 2016 Evaluation Campaign featured two tasks: the translation of talks and the translation of video conference conversations.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
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).
no code implementations • 24 Jan 2023 • Andrea Piergentili, Dennis Fucci, Beatrice Savoldi, Luisa Bentivogli, Matteo Negri
Gender inclusivity in language technologies has become a prominent research topic.
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.
1 code implementation • 15 Sep 2021 • Marco Gaido, Susana Rodríguez, Matteo Negri, Luisa Bentivogli, Marco Turchi
Automatic translation systems are known to struggle with rare words.
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.
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.
1 code implementation • 13 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.
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.
no code implementations • ACL 2020 • Luisa Bentivogli, Beatrice Savoldi, Matteo Negri, Mattia Antonino Di Gangi, Roldano Cattoni, Marco Turchi
Translating from languages without productive grammatical gender like English into gender-marked languages is a well-known difficulty for machines.
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.
no code implementations • NAACL 2019 • Mattia A. Di Gangi, Roldano Cattoni, Luisa Bentivogli, Matteo Negri, Marco Turchi
Current research on spoken language translation (SLT) has to confront with the scarcity of sizeable and publicly available training corpora.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
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).
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.
no code implementations • LREC 2014 • Marco Marelli, Stefano Menini, Marco Baroni, Luisa Bentivogli, Raffaella Bernardi, Roberto Zamparelli
Shared and internationally recognized benchmarks are fundamental for the development of any computational system.
no code implementations • LREC 2012 • Marcello Federico, Sebastian St{\"u}ker, Luisa Bentivogli, Michael Paul, Mauro Cettolo, Teresa Herrmann, Jan Niehues, Giovanni Moretti
We report here on the eighth evaluation campaign organized in 2011 by the IWSLT workshop series.
no code implementations • LREC 2012 • Matteo Negri, Yashar Mehdad, Aless Marchetti, ro, Danilo Giampiccolo, Luisa Bentivogli
We present a framework for the acquisition of sentential paraphrases based on crowdsourcing.