21 research outputs found
ON-TRAC Consortium End-to-End Speech Translation Systems for the IWSLT 2019 Shared Task
International audienceThis paper describes the ON-TRAC Consortium translation systems developed for the end-to-end model task of IWSLT Evaluation 2019 for the English→ Portuguese language pair. ON-TRAC Consortium is composed of researchers from three French academic laboratories: LIA (Avignon Univer-sité), LIG (Université Grenoble Alpes), and LIUM (Le Mans Université). A single end-to-end model built as a neural encoder-decoder architecture with attention mechanism was used for two primary submissions corresponding to the two EN-PT evaluations sets: (1) TED (MuST-C) and (2) How2. In this paper, we notably investigate impact of pooling heterogeneous corpora for training, impact of target tokeniza-tion (characters or BPEs), impact of speech input segmenta-tion and we also compare our best end-to-end model (BLEU of 26.91 on MuST-C and 43.82 on How2 validation sets) to a pipeline (ASR+MT) approach
ON-TRAC Consortium for End-to-End and Simultaneous Speech Translation Challenge Tasks at IWSLT 2020
This paper describes the ON-TRAC Consortium translation systems developed for
two challenge tracks featured in the Evaluation Campaign of IWSLT 2020, offline
speech translation and simultaneous speech translation. ON-TRAC Consortium is
composed of researchers from three French academic laboratories: LIA (Avignon
Universit\'e), LIG (Universit\'e Grenoble Alpes), and LIUM (Le Mans
Universit\'e). Attention-based encoder-decoder models, trained end-to-end, were
used for our submissions to the offline speech translation track. Our
contributions focused on data augmentation and ensembling of multiple models.
In the simultaneous speech translation track, we build on Transformer-based
wait-k models for the text-to-text subtask. For speech-to-text simultaneous
translation, we attach a wait-k MT system to a hybrid ASR system. We propose an
algorithm to control the latency of the ASR+MT cascade and achieve a good
latency-quality trade-off on both subtasks
Impact of Encoding and Segmentation Strategies on End-to-End Simultaneous Speech Translation
Boosted by the simultaneous translation shared task at IWSLT 2020, promising
end-to-end online speech translation approaches were recently proposed. They
consist in incrementally encoding a speech input (in a source language) and
decoding the corresponding text (in a target language) with the best possible
trade-off between latency and translation quality. This paper investigates two
key aspects of end-to-end simultaneous speech translation: (a) how to encode
efficiently the continuous speech flow, and (b) how to segment the speech flow
in order to alternate optimally between reading (R: encoding input) and writing
(W: decoding output) operations. We extend our previously proposed end-to-end
online decoding strategy and show that while replacing BLSTM by ULSTM encoding
degrades performance in offline mode, it actually improves both efficiency and
performance in online mode. We also measure the impact of different methods to
segment the speech signal (using fixed interval boundaries, oracle word
boundaries or randomly set boundaries) and show that our best end-to-end online
decoding strategy is surprisingly the one that alternates R/W operations on
fixed size blocks on our English-German speech translation setup.Comment: Accepted for presentation at Interspeech 202
Findings of the IWSLT 2022 Evaluation Campaign.
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. A total of 27 teams participated in at least one of the shared tasks. This paper details, for each shared task, the purpose of the task, the data that were released, the evaluation metrics that were applied, the submissions that were received and the results that were achieved
Findings of the iWSLT 2023 evaluation campaign
This paper reports on the shared tasks organized by the 20th IWSLT Conference. The shared tasks address 9 scientific challenges in spoken language translation: simultaneous and offline translation, automatic subtitling and dubbing, speech-to-speech translation, multilingual, dialect and low-resource speech translation, and formality control. The shared tasks attracted a total of 38 submissions by 31 teams. The growing interest towards spoken language translation is also witnessed by the constantly increasing number of shared task organizers and contributors to the overview paper, almost evenly distributed across industry and academia.peer-reviewe