Zobrazeno 1 - 10
of 37
pro vyhledávání: '"Papi, Sara"'
Autor:
Ahmad, Ibrahim Said, Anastasopoulos, Antonios, Bojar, Ondřej, Borg, Claudia, Carpuat, Marine, Cattoni, Roldano, Cettolo, Mauro, Chen, William, Dong, Qianqian, Federico, Marcello, Haddow, Barry, Javorský, Dávid, Krubiński, Mateusz, Lam, Tsz Kin, Ma, Xutai, Mathur, Prashant, Matusov, Evgeny, Maurya, Chandresh, McCrae, John, Murray, Kenton, Nakamura, Satoshi, Negri, Matteo, Niehues, Jan, Niu, Xing, Ojha, Atul Kr., Ortega, John, Papi, Sara, Polák, Peter, Pospíšil, Adam, Pecina, Pavel, Salesky, Elizabeth, Sethiya, Nivedita, Sarkar, Balaram, Shi, Jiatong, Sikasote, Claytone, Sperber, Matthias, Stüker, Sebastian, Sudoh, Katsuhito, Thompson, Brian, Turchi, Marco, Waibel, Alex, Watanabe, Shinji, Wilken, Patrick, Zemánek, Petr, Zevallos, Rodolfo
This paper reports on the shared tasks organized by the 21st IWSLT Conference. The shared tasks address 7 scientific challenges in spoken language translation: simultaneous and offline translation, automatic subtitling and dubbing, speech-to-speech t
Externí odkaz:
http://arxiv.org/abs/2411.05088
Gender bias in machine translation (MT) is recognized as an issue that can harm people and society. And yet, advancements in the field rarely involve people, the final MT users, or inform how they might be impacted by biased technologies. Current eva
Externí odkaz:
http://arxiv.org/abs/2410.00545
MOSEL: 950,000 Hours of Speech Data for Open-Source Speech Foundation Model Training on EU Languages
Autor:
Gaido, Marco, Papi, Sara, Bentivogli, Luisa, Brutti, Alessio, Cettolo, Mauro, Gretter, Roberto, Matassoni, Marco, Nabih, Mohamed, Negri, Matteo
The rise of foundation models (FMs), coupled with regulatory efforts addressing their risks and impacts, has sparked significant interest in open-source models. However, existing speech FMs (SFMs) fall short of full compliance with the open-source pr
Externí odkaz:
http://arxiv.org/abs/2410.01036
Autor:
Verdini, Francesco, Melucci, Pierfrancesco, Perna, Stefano, Cariaggi, Francesco, Gaido, Marco, Papi, Sara, Mazurek, Szymon, Kasztelnik, Marek, Bentivogli, Luisa, Bratières, Sébastien, Merialdo, Paolo, Scardapane, Simone
The remarkable performance achieved by Large Language Models (LLM) has driven research efforts to leverage them for a wide range of tasks and input modalities. In speech-to-text (S2T) tasks, the emerging solution consists of projecting the output of
Externí odkaz:
http://arxiv.org/abs/2409.17044
This paper describes the FBK's participation in the Simultaneous Translation Evaluation Campaign at IWSLT 2024. For this year's submission in the speech-to-text translation (ST) sub-track, we propose SimulSeamless, which is realized by combining Alig
Externí odkaz:
http://arxiv.org/abs/2406.14177
Streaming speech-to-text translation (StreamST) is the task of automatically translating speech while incrementally receiving an audio stream. Unlike simultaneous ST (SimulST), which deals with pre-segmented speech, StreamST faces the challenges of h
Externí odkaz:
http://arxiv.org/abs/2406.06097
Subtitling plays a crucial role in enhancing the accessibility of audiovisual content and encompasses three primary subtasks: translating spoken dialogue, segmenting translations into concise textual units, and estimating timestamps that govern their
Externí odkaz:
http://arxiv.org/abs/2405.10741
The attention mechanism, a cornerstone of state-of-the-art neural models, faces computational hurdles in processing long sequences due to its quadratic complexity. Consequently, research efforts in the last few years focused on finding more efficient
Externí odkaz:
http://arxiv.org/abs/2402.13208
The field of natural language processing (NLP) has recently witnessed a transformative shift with the emergence of foundation models, particularly Large Language Models (LLMs) that have revolutionized text-based NLP. This paradigm has extended to oth
Externí odkaz:
http://arxiv.org/abs/2402.12025
When translating words referring to the speaker, speech translation (ST) systems should not resort to default masculine generics nor rely on potentially misleading vocal traits. Rather, they should assign gender according to the speakers' preference.
Externí odkaz:
http://arxiv.org/abs/2310.15752