Peter Polák


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Milind Agarwal | Sweta Agrawal | Antonios Anastasopoulos | Luisa Bentivogli | Ondřej Bojar | Claudia Borg | Marine Carpuat | Roldano Cattoni | Mauro Cettolo | Mingda Chen | William Chen | Khalid Choukri | Alexandra Chronopoulou | Anna Currey | Thierry Declerck | Qianqian Dong | Kevin Duh | Yannick Estève | Marcello Federico | Souhir Gahbiche | Barry Haddow | Benjamin Hsu | Phu Mon Htut | Hirofumi Inaguma | Dávid Javorský | John Judge | Yasumasa Kano | Tom Ko | Rishu Kumar | Pengwei Li | Xutai Ma | Prashant Mathur | Evgeny Matusov | Paul McNamee | John P. McCrae | Kenton Murray | Maria Nadejde | Satoshi Nakamura | Matteo Negri | Ha Nguyen | Jan Niehues | Xing Niu | Atul Kr. Ojha | John E. Ortega | Proyag Pal | Juan Pino | Lonneke van der Plas | Peter Polák | Elijah Rippeth | Elizabeth Salesky | Jiatong Shi | Matthias Sperber | Sebastian Stüker | Katsuhito Sudoh | Yun Tang | Brian Thompson | Kevin Tran | Marco Turchi | Alex Waibel | Mingxuan Wang | Shinji Watanabe | Rodolfo Zevallos
Proceedings of the 20th International Conference on Spoken Language Translation (IWSLT 2023)

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.

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Towards Efficient Simultaneous Speech Translation: CUNI-KIT System for Simultaneous Track at IWSLT 2023
Peter Polák | Danni Liu | Ngoc-Quan Pham | Jan Niehues | Alexander Waibel | Ondřej Bojar
Proceedings of the 20th International Conference on Spoken Language Translation (IWSLT 2023)

In this paper, we describe our submission to the Simultaneous Track at IWSLT 2023. This year, we continue with the successful setup from the last year, however, we adopt the latest methods that further improve the translation quality. Additionally, we propose a novel online policy for attentional encoder-decoder models. The policy prevents the model to generate translation beyond the current speech input by using an auxiliary CTC output layer. We show that the proposed simultaneous policy can be applied to both streaming blockwise models and offline encoder-decoder models. We observe significant improvements in quality (up to 1.1 BLEU) and the computational footprint (up to 45% relative RTF).

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Robustness of Multi-Source MT to Transcription Errors
Dominik Macháček | Peter Polák | Ondřej Bojar | Raj Dabre
Findings of the Association for Computational Linguistics: ACL 2023

Automatic speech translation is sensitive to speech recognition errors, but in a multilingual scenario, the same content may be available in various languages via simultaneous interpreting, dubbing or subtitling. In this paper, we hypothesize that leveraging multiple sources will improve translation quality if the sources complement one another in terms of correct information they contain. To this end, we first show that on a 10-hour ESIC corpus, the ASR errors in the original English speech and its simultaneous interpreting into German and Czech are mutually independent. We then use two sources, English and German, in a multi-source setting for translation into Czech to establish its robustness to ASR errors. Furthermore, we observe this robustness when translating both noisy sources together in a simultaneous translation setting. Our results show that multi-source neural machine translation has the potential to be useful in a real-time simultaneous translation setting, thereby motivating further investigation in this area.

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ESPnet-ST-v2: Multipurpose Spoken Language Translation Toolkit
Brian Yan | Jiatong Shi | Yun Tang | Hirofumi Inaguma | Yifan Peng | Siddharth Dalmia | Peter Polák | Patrick Fernandes | Dan Berrebbi | Tomoki Hayashi | Xiaohui Zhang | Zhaoheng Ni | Moto Hira | Soumi Maiti | Juan Pino | Shinji Watanabe
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)

ESPnet-ST-v2 is a revamp of the open-source ESPnet-ST toolkit necessitated by the broadening interests of the spoken language translation community. ESPnet-ST-v2 supports 1) offline speech-to-text translation (ST), 2) simultaneous speech-to-text translation (SST), and 3) offline speech-to-speech translation (S2ST) – each task is supported with a wide variety of approaches, differentiating ESPnet-ST-v2 from other open source spoken language translation toolkits. This toolkit offers state-of-the-art architectures such as transducers, hybrid CTC/attention, multi-decoders with searchable intermediates, time-synchronous blockwise CTC/attention, Translatotron models, and direct discrete unit models. In this paper, we describe the overall design, example models for each task, and performance benchmarking behind ESPnet-ST-v2, which is publicly available at


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CUNI-KIT System for Simultaneous Speech Translation Task at IWSLT 2022
Peter Polák | Ngoc-Quan Pham | Tuan Nam Nguyen | Danni Liu | Carlos Mullov | Jan Niehues | Ondřej Bojar | Alexander Waibel
Proceedings of the 19th International Conference on Spoken Language Translation (IWSLT 2022)

In this paper, we describe our submission to the Simultaneous Speech Translation at IWSLT 2022. We explore strategies to utilize an offline model in a simultaneous setting without the need to modify the original model. In our experiments, we show that our onlinization algorithm is almost on par with the offline setting while being 3x faster than offline in terms of latency on the test set. We also show that the onlinized offline model outperforms the best IWSLT2021 simultaneous system in medium and high latency regimes and is almost on par in the low latency regime. We make our system publicly available.

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ALIGNMEET: A Comprehensive Tool for Meeting Annotation, Alignment, and Evaluation
Peter Polák | Muskaan Singh | Anna Nedoluzhko | Ondřej Bojar
Proceedings of the Thirteenth Language Resources and Evaluation Conference

Summarization is a challenging problem, and even more challenging is to manually create, correct, and evaluate the summaries. The severity of the problem grows when the inputs are multi-party dialogues in a meeting setup. To facilitate the research in this area, we present ALIGNMEET, a comprehensive tool for meeting annotation, alignment, and evaluation. The tool aims to provide an efficient and clear interface for fast annotation while mitigating the risk of introducing errors. Moreover, we add an evaluation mode that enables a comprehensive quality evaluation of meeting minutes. To the best of our knowledge, there is no such tool available. We release the tool as open source. It is also directly installable from PyPI.


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ELITR Multilingual Live Subtitling: Demo and Strategy
Ondřej Bojar | Dominik Macháček | Sangeet Sagar | Otakar Smrž | Jonáš Kratochvíl | Peter Polák | Ebrahim Ansari | Mohammad Mahmoudi | Rishu Kumar | Dario Franceschini | Chiara Canton | Ivan Simonini | Thai-Son Nguyen | Felix Schneider | Sebastian Stüker | Alex Waibel | Barry Haddow | Rico Sennrich | Philip Williams
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations

This paper presents an automatic speech translation system aimed at live subtitling of conference presentations. We describe the overall architecture and key processing components. More importantly, we explain our strategy for building a complex system for end-users from numerous individual components, each of which has been tested only in laboratory conditions. The system is a working prototype that is routinely tested in recognizing English, Czech, and German speech and presenting it translated simultaneously into 42 target languages.

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Explainable Quality Estimation: CUNI Eval4NLP Submission
Peter Polák | Muskaan Singh | Ondřej Bojar
Proceedings of the 2nd Workshop on Evaluation and Comparison of NLP Systems

This paper describes our participating system in the shared task Explainable quality estimation of 2nd Workshop on Evaluation & Comparison of NLP Systems. The task of quality estimation (QE, a.k.a. reference-free evaluation) is to predict the quality of MT output at inference time without access to reference translations. In this proposed work, we first build a word-level quality estimation model, then we finetune this model for sentence-level QE. Our proposed models achieve near state-of-the-art results. In the word-level QE, we place 2nd and 3rd on the supervised Ro-En and Et-En test sets. In the sentence-level QE, we achieve a relative improvement of 8.86% (Ro-En) and 10.6% (Et-En) in terms of the Pearson correlation coefficient over the baseline model.


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Large Corpus of Czech Parliament Plenary Hearings
Jonáš Kratochvil | Peter Polák | Ondřej Bojar
Proceedings of the Twelfth Language Resources and Evaluation Conference

We present a large corpus of Czech parliament plenary sessions. The corpus consists of approximately 1200 hours of speech data and corresponding text transcriptions. The whole corpus has been segmented to short audio segments making it suitable for both training and evaluation of automatic speech recognition (ASR) systems. The source language of the corpus is Czech, which makes it a valuable resource for future research as only a few public datasets are available in the Czech language. We complement the data release with experiments of two baseline ASR systems trained on the presented data: the more traditional approach implemented in the Kaldi ASRtoolkit which combines hidden Markov models and deep neural networks (NN) and a modern ASR architecture implemented in Jaspertoolkit which uses deep NNs in an end-to-end fashion.

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CUNI Neural ASR with Phoneme-Level Intermediate Step for~Non-Native~SLT at IWSLT 2020
Peter Polák | Sangeet Sagar | Dominik Macháček | Ondřej Bojar
Proceedings of the 17th International Conference on Spoken Language Translation

In this paper, we present our submission to the Non-Native Speech Translation Task for IWSLT 2020. Our main contribution is a proposed speech recognition pipeline that consists of an acoustic model and a phoneme-to-grapheme model. As an intermediate representation, we utilize phonemes. We demonstrate that the proposed pipeline surpasses commercially used automatic speech recognition (ASR) and submit it into the ASR track. We complement this ASR with off-the-shelf MT systems to take part also in the speech translation track.