no code implementations • NAACL 2022 • Jindřich Helcl, Barry Haddow, Alexandra Birch
In this paper, we point out flaws in the evaluation methodology present in the literature on NAR models and we provide a fair comparison between a state-of-the-art NAR model and the autoregressive submissions to the shared task.
no code implementations • IWSLT 2017 • Pawel Przybysz, Marcin Chochowski, Rico Sennrich, Barry Haddow, Alexandra Birch
This paper describes the joint submission of Samsung Research and Development, Warsaw, Poland and the University of Edinburgh team to the IWSLT MT task for TED talks.
no code implementations • ACL (IWSLT) 2021 • Sukanta Sen, Ulrich Germann, Barry Haddow
We describe our submission to the IWSLT 2021 shared task on simultaneous text-to-text English-German translation.
no code implementations • MTSummit 2021 • Ondřej Bojar, Vojtěch Srdečný, Rishu Kumar, Otakar Smrž, Felix Schneider, Barry Haddow, Phil Williams, Chiara Canton
We describe our experience with providing automatic simultaneous spoken language translation for an event with human interpreters.
no code implementations • MTSummit 2021 • Alexandra Birch, Barry Haddow, Antonio Valerio Miceli Barone, Jindrich Helcl, Jonas Waldendorf, Felipe Sánchez Martínez, Mikel Forcada, Víctor Sánchez Cartagena, Juan Antonio Pérez-Ortiz, Miquel Esplà-Gomis, Wilker Aziz, Lina Murady, Sevi Sariisik, Peggy van der Kreeft, Kay Macquarrie
We find that starting from an existing large model pre-trained on 50languages leads to far better BLEU scores than pretraining on one high-resource language pair with a smaller model.
no code implementations • IWSLT (EMNLP) 2018 • Philip Williams, Marcin Chochowski, Pawel Przybysz, Rico Sennrich, Barry Haddow, Alexandra Birch
This paper describes the joint submission to the IWSLT 2018 Low Resource MT task by Samsung R&D Institute, Poland, and the University of Edinburgh.
no code implementations • EMNLP 2020 • Yvette Graham, Barry Haddow, Philipp Koehn
In addition, we provide a re-evaluation of a past machine translation evaluation claiming human-parity of MT.
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 • WMT (EMNLP) 2021 • Farhad Akhbardeh, Arkady Arkhangorodsky, Magdalena Biesialska, Ondřej Bojar, Rajen Chatterjee, Vishrav Chaudhary, Marta R. Costa-Jussa, Cristina España-Bonet, Angela Fan, Christian Federmann, Markus Freitag, Yvette Graham, Roman Grundkiewicz, Barry Haddow, Leonie Harter, Kenneth Heafield, Christopher Homan, Matthias Huck, Kwabena Amponsah-Kaakyire, Jungo Kasai, Daniel Khashabi, Kevin Knight, Tom Kocmi, Philipp Koehn, Nicholas Lourie, Christof Monz, Makoto Morishita, Masaaki Nagata, Ajay Nagesh, Toshiaki Nakazawa, Matteo Negri, Santanu Pal, Allahsera Auguste Tapo, Marco Turchi, Valentin Vydrin, Marcos Zampieri
This paper presents the results of the newstranslation task, the multilingual low-resourcetranslation for Indo-European languages, thetriangular translation task, and the automaticpost-editing task organised as part of the Con-ference on Machine Translation (WMT) 2021. In the news task, participants were asked tobuild machine translation systems for any of10 language pairs, to be evaluated on test setsconsisting mainly of news stories.
no code implementations • EAMT 2020 • Ondřej Bojar, Dominik Macháček, Sangeet Sagar, Otakar Smrž, Jonáš Kratochvíl, Ebrahim Ansari, Dario Franceschini, Chiara Canton, Ivan Simonini, Thai-Son Nguyen, Felix Schneider, Sebastian Stücker, Alex Waibel, Barry Haddow, Rico Sennrich, Philip Williams
ELITR (European Live Translator) project aims to create a speech translation system for simultaneous subtitling of conferences and online meetings targetting up to 43 languages.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +3
no code implementations • EMNLP (IWSLT) 2019 • Joanna Wetesko, Marcin Chochowski, Pawel Przybysz, Philip Williams, Roman Grundkiewicz, Rico Sennrich, Barry Haddow, None Barone, Valerio Miceli, Alexandra Birch
This paper describes the joint submission to the IWSLT 2019 English to Czech task by Samsung RD Institute, Poland, and the University of Edinburgh.
no code implementations • 22 Apr 2024 • Dawei Zhu, Pinzhen Chen, Miaoran Zhang, Barry Haddow, Xiaoyu Shen, Dietrich Klakow
Traditionally, success in multilingual machine translation can be attributed to three key factors in training data: large volume, diverse translation directions, and high quality.
no code implementations • 29 Feb 2024 • Tsz Kin Lam, Alexandra Birch, Barry Haddow
In this paper, we leverage the SSL models by pretraining smaller models on their Discrete Speech Units (DSU).
no code implementations • 1 Feb 2024 • Giulio Zhou, Tsz Kin Lam, Alexandra Birch, Barry Haddow
While there has been a growing interest in developing direct speech translation systems to avoid propagating errors and losing non-verbal content, prior work in direct S2TT has struggled to conclusively establish the advantages of integrating the acoustic signal directly into the translation process.
1 code implementation • 20 Dec 2023 • Weixuan Wang, Barry Haddow, Alexandra Birch
Knowledge represented in Large Language Models (LLMs) is quite often incorrect and can also become obsolete over time.
2 code implementations • 24 Nov 2023 • Nikolay Bogoychev, Jelmer Van der Linde, Graeme Nail, Barry Haddow, Jaume Zaragoza-Bernabeu, Gema Ramírez-Sánchez, Lukas Weymann, Tudor Nicolae Mateiu, Jindřich Helcl, Mikko Aulamo
Developing high quality machine translation systems is a labour intensive, challenging and confusing process for newcomers to the field.
no code implementations • 16 Nov 2023 • Nikolay Bogoychev, Pinzhen Chen, Barry Haddow, Alexandra Birch
Large language model (LLM) inference is computation and memory intensive, so we adapt lexical shortlisting to it hoping to improve both.
1 code implementation • 15 Oct 2023 • Weixuan Wang, Barry Haddow, Alexandra Birch, Wei Peng
Large language models (LLMs) have been treated as knowledge bases due to their strong performance in knowledge probing tasks.
1 code implementation • 16 Sep 2023 • Pinzhen Chen, Shaoxiong Ji, Nikolay Bogoychev, Andrey Kutuzov, Barry Haddow, Kenneth Heafield
Foundational large language models (LLMs) can be instruction-tuned to perform open-domain question answering, facilitating applications like chat assistants.
no code implementations • 6 Jun 2023 • Pinzhen Chen, Zhicheng Guo, Barry Haddow, Kenneth Heafield
In this paper, we propose iterative translation refinement to leverage the power of large language models for more natural translation and post-editing.
no code implementations • 23 May 2023 • Christos Baziotis, Biao Zhang, Alexandra Birch, Barry Haddow
Next, we analyze the impact of scale (from 90M to 1. 6B parameters) and find it is important for both methods, particularly DAE.
1 code implementation • 15 May 2023 • Ashok Urlana, Pinzhen Chen, Zheng Zhao, Shay B. Cohen, Manish Shrivastava, Barry Haddow
This paper introduces PMIndiaSum, a multilingual and massively parallel summarization corpus focused on languages in India.
1 code implementation • 28 Mar 2023 • Nuno M. Guerreiro, Duarte Alves, Jonas Waldendorf, Barry Haddow, Alexandra Birch, Pierre Colombo, André F. T. Martins
Large-scale multilingual machine translation systems have demonstrated remarkable ability to translate directly between numerous languages, making them increasingly appealing for real-world applications.
1 code implementation • 21 Feb 2023 • Biao Zhang, Barry Haddow, Rico Sennrich
For end-to-end speech translation, regularizing the encoder with the Connectionist Temporal Classification (CTC) objective using the source transcript or target translation as labels can greatly improve quality metrics.
no code implementations • 17 Jan 2023 • Biao Zhang, Barry Haddow, Alexandra Birch
Research on prompting has shown excellent performance with little or even no supervised training across many tasks.
1 code implementation • 24 Oct 2022 • Chantal Amrhein, Barry Haddow
For real-life applications, it is crucial that end-to-end spoken language translation models perform well on continuous audio, without relying on human-supplied segmentation.
no code implementations • 18 Oct 2022 • Sukanta Sen, Ondřej Bojar, Barry Haddow
In the cascaded approach to spoken language translation (SLT), the ASR output is typically punctuated and segmented into sentences before being passed to MT, since the latter is typically trained on written text.
1 code implementation • 9 Jun 2022 • Biao Zhang, Barry Haddow, Rico Sennrich
Finally, we discuss neural acoustic feature modeling, where a neural model is designed to extract acoustic features from raw speech signals directly, with the goal to simplify inductive biases and add freedom to the model in describing speech.
no code implementations • NAACL 2022 • Arturo Oncevay, Duygu Ataman, Niels van Berkel, Barry Haddow, Alexandra Birch, Johannes Bjerva
In this work, we propose to reduce the rigidity of such claims, by quantifying morphological typology at the word and segment level.
no code implementations • 4 May 2022 • Jindřich Helcl, Barry Haddow, Alexandra Birch
In this paper, we point out flaws in the evaluation methodology present in the literature on NAR models and we provide a fair comparison between a state-of-the-art NAR model and the autoregressive submissions to the shared task.
no code implementations • 15 Sep 2021 • Philip Williams, Barry Haddow
We present the ELITR ECA corpus, a multilingual corpus derived from publications of the European Court of Auditors.
no code implementations • CL (ACL) 2022 • Barry Haddow, Rachel Bawden, Antonio Valerio Miceli Barone, Jindřich Helcl, Alexandra Birch
We present a survey covering the state of the art in low-resource machine translation research.
1 code implementation • ACL 2021 • Biao Zhang, Ivan Titov, Barry Haddow, Rico Sennrich
Document-level contextual information has shown benefits to text-based machine translation, but whether and how context helps end-to-end (E2E) speech translation (ST) is still under-studied.
1 code implementation • Findings (ACL) 2021 • Christos Baziotis, Ivan Titov, Alexandra Birch, Barry Haddow
Unsupervised cross-lingual pretraining has achieved strong results in neural machine translation (NMT), by drastically reducing the need for large parallel data.
1 code implementation • EACL 2021 • Ebrahim Ansari, Ond{\v{r}}ej Bojar, Barry Haddow, Mohammad Mahmoudi
SLTev reports the quality, latency, and stability of an SLT candidate output based on the time-stamped transcript and reference translation into a target language.
no code implementations • EACL 2021 • Ond{\v{r}}ej Bojar, Dominik Mach{\'a}{\v{c}}ek, Sangeet Sagar, Otakar Smr{\v{z}}, Jon{\'a}{\v{s}} Kratochv{\'\i}l, Peter Pol{\'a}k, Ebrahim Ansari, Mohammad Mahmoudi, Rishu Kumar, Dario Franceschini, Chiara Canton, Ivan Simonini, Thai-Son Nguyen, Felix Schneider, Sebastian St{\"u}ker, Alex Waibel, Barry Haddow, Rico Sennrich, Philip Williams
This paper presents an automatic speech translation system aimed at live subtitling of conference presentations.
no code implementations • EMNLP 2020 • Loïc Barrault, Magdalena Biesialska, Ondřej Bojar, Marta R. Costa-jussà, Christian Federmann, Yvette Graham, Roman Grundkiewicz, Barry Haddow, Matthias Huck, Eric Joanis, Tom Kocmi, Philipp Koehn, Chi-kiu Lo, Nikola Ljubešić, Christof Monz, Makoto Morishita, Masaaki Nagata, Toshiaki Nakazawa, Santanu Pal, Matt Post, Marcos Zampieri
In the news task, participants were asked to build machine translation systems for any of 11 language pairs, to be evaluated on test sets consisting mainly of news stories.
no code implementations • Findings of the Association for Computational Linguistics 2020 • Yvette Graham, Christian Federmann, Maria Eskevich, Barry Haddow
Recent machine translation shared tasks have shown top-performing systems to tie or in some cases even outperform human translation.
1 code implementation • Findings of the Association for Computational Linguistics 2020 • Biao Zhang, Ivan Titov, Barry Haddow, Rico Sennrich
Information in speech signals is not evenly distributed, making it an additional challenge for end-to-end (E2E) speech translation (ST) to learn to focus on informative features.
2 code implementations • ACL 2020 • Marta Ba{\~n}{\'o}n, Pin-zhen Chen, Barry Haddow, Kenneth Heafield, Hieu Hoang, Miquel Espl{\`a}-Gomis, Mikel L. Forcada, Amir Kamran, Faheem Kirefu, Philipp Koehn, Sergio Ortiz Rojas, Leopoldo Pla Sempere, Gema Ram{\'\i}rez-S{\'a}nchez, Elsa Sarr{\'\i}as, Marek Strelec, Brian Thompson, William Waites, Dion Wiggins, Jaume Zaragoza
We report on methods to create the largest publicly available parallel corpora by crawling the web, using open source software.
no code implementations • 30 May 2020 • Yuekun Yao, Barry Haddow
For spoken language translation (SLT) in live scenarios such as conferences, lectures and meetings, it is desirable to show the translation to the user as quickly as possible, avoiding an annoying lag between speaker and translated captions.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +3
no code implementations • LREC 2020 • Dario Franceschini, Chiara Canton, Ivan Simonini, Armin Schweinfurth, Adelheid Glott, Sebastian St{\"u}ker, Thai-Son Nguyen, Felix Schneider, Thanh-Le Ha, Alex Waibel, Barry Haddow, Philip Williams, Rico Sennrich, Ond{\v{r}}ej Bojar, Sangeet Sagar, Dominik Mach{\'a}{\v{c}}ek, Otakar Smr{\v{z}}
This paper presents our progress towards deploying a versatile communication platform in the task of highly multilingual live speech translation for conferences and remote meetings live subtitling.
no code implementations • LREC 2020 • Susie Coleman, Andrew Secker, Rachel Bawden, Barry Haddow, Alex Birch, ra
A growth in news sources makes this increasingly challenging and time-consuming but MT can help automate some aspects of this process.
1 code implementation • EMNLP 2020 • Christos Baziotis, Barry Haddow, Alexandra Birch
A common solution is to exploit the knowledge of language models (LM) trained on abundant monolingual data.
1 code implementation • EMNLP 2020 • Arturo Oncevay, Barry Haddow, Alexandra Birch
Sparse language vectors from linguistic typology databases and learned embeddings from tasks like multilingual machine translation have been investigated in isolation, without analysing how they could benefit from each other's language characterisation.
2 code implementations • 27 Jan 2020 • Barry Haddow, Faheem Kirefu
Parallel text is required for building high-quality machine translation (MT) systems, as well as for other multilingual NLP applications.
no code implementations • WS 2019 • Alex Birch, ra, Barry Haddow, Ivan Tito, Antonio Valerio Miceli Barone, Rachel Bawden, Felipe S{\'a}nchez-Mart{\'\i}nez, Mikel L. Forcada, Miquel Espl{\`a}-Gomis, V{\'\i}ctor S{\'a}nchez-Cartagena, Juan Antonio P{\'e}rez-Ortiz, Wilker Aziz, Andrew Secker, Peggy van der Kreeft
no code implementations • WS 2019 • Lo{\"\i}c Barrault, Ond{\v{r}}ej Bojar, Marta R. Costa-juss{\`a}, Christian Federmann, Mark Fishel, Yvette Graham, Barry Haddow, Matthias Huck, Philipp Koehn, Shervin Malmasi, Christof Monz, Mathias M{\"u}ller, Santanu Pal, Matt Post, Marcos Zampieri
This paper presents the results of the premier shared task organized alongside the Conference on Machine Translation (WMT) 2019.
no code implementations • 24 Jun 2019 • Yvette Graham, Barry Haddow, Philipp Koehn
Finally, we provide a comprehensive check-list for future machine translation evaluation.
no code implementations • WS 2018 • Ond{\v{r}}ej Bojar, Christian Federmann, Mark Fishel, Yvette Graham, Barry Haddow, Philipp Koehn, Christof Monz
This paper presents the results of the premier shared task organized alongside the Conference on Machine Translation (WMT) 2018.
no code implementations • EMNLP 2018 • Ond{\v{r}}ej Bojar, Rajen Chatterjee, Christian Federmann, Mark Fishel, Yvette Graham, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Philipp Koehn, Christof Monz, Matteo Negri, Aur{\'e}lie N{\'e}v{\'e}ol, Mariana Neves, Matt Post, Lucia Specia, Marco Turchi, Karin Verspoor
no code implementations • WS 2018 • Barry Haddow, Nikolay Bogoychev, Denis Emelin, Ulrich Germann, Roman Grundkiewicz, Kenneth Heafield, Antonio Valerio Miceli Barone, Rico Sennrich
The University of Edinburgh made submissions to all 14 language pairs in the news translation task, with strong performances in most pairs.
no code implementations • WS 2018 • Mikel L. Forcada, Carolina Scarton, Lucia Specia, Barry Haddow, Alexandra Birch
A popular application of machine translation (MT) is gisting: MT is consumed as is to make sense of text in a foreign language.
no code implementations • NAACL 2018 • Rachel Bawden, Rico Sennrich, Alexandra Birch, Barry Haddow
Despite gains using BLEU, multi-encoder models give limited improvement in the handling of discourse phenomena: 50% accuracy on our coreference test set and 53. 5% for coherence/cohesion (compared to a non-contextual baseline of 50%).
no code implementations • WS 2017 • Ond{\v{r}}ej Bojar, Rajen Chatterjee, Christian Federmann, Yvette Graham, Barry Haddow, Shu-Jian Huang, Matthias Huck, Philipp Koehn, Qun Liu, Varvara Logacheva, Christof Monz, Matteo Negri, Matt Post, Raphael Rubino, Lucia Specia, Marco Turchi
no code implementations • WS 2017 • Antonio Jimeno Yepes, Aur{\'e}lie N{\'e}v{\'e}ol, Mariana Neves, Karin Verspoor, Ond{\v{r}}ej Bojar, Arthur Boyer, Cristian Grozea, Barry Haddow, Madeleine Kittner, Yvonne Lichtblau, Pavel Pecina, Rol Roller, , Rudolf Rosa, Amy Siu, Philippe Thomas, Saskia Trescher
no code implementations • WS 2017 • Rico Sennrich, Alexandra Birch, Anna Currey, Ulrich Germann, Barry Haddow, Kenneth Heafield, Antonio Valerio Miceli Barone, Philip Williams
This paper describes the University of Edinburgh's submissions to the WMT17 shared news translation and biomedical translation tasks.
no code implementations • EMNLP 2017 • Antonio Valerio Miceli Barone, Barry Haddow, Ulrich Germann, Rico Sennrich
We investigate techniques for supervised domain adaptation for neural machine translation where an existing model trained on a large out-of-domain dataset is adapted to a small in-domain dataset.
3 code implementations • WS 2017 • Antonio Valerio Miceli Barone, Jindřich Helcl, Rico Sennrich, Barry Haddow, Alexandra Birch
It has been shown that increasing model depth improves the quality of neural machine translation.
no code implementations • EACL 2017 • Rico Sennrich, Barry Haddow
Neural Machine Translation (NMT) has achieved new breakthroughs in machine translation in recent years.
4 code implementations • EACL 2017 • Rico Sennrich, Orhan Firat, Kyunghyun Cho, Alexandra Birch, Barry Haddow, Julian Hitschler, Marcin Junczys-Dowmunt, Samuel Läubli, Antonio Valerio Miceli Barone, Jozef Mokry, Maria Nădejde
We present Nematus, a toolkit for Neural Machine Translation.
no code implementations • WS 2016 • Ond{\v{r}}ej Bojar, Christian Buck, Rajen Chatterjee, Christian Federmann, Liane Guillou, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Aur{\'e}lie N{\'e}v{\'e}ol, Mariana Neves, Pavel Pecina, Martin Popel, Philipp Koehn, Christof Monz, Matteo Negri, Matt Post, Lucia Specia, Karin Verspoor, J{\"o}rg Tiedemann, Marco Turchi
no code implementations • WS 2016 • Ond{\v{r}}ej Bojar, Rajen Chatterjee, Christian Federmann, Yvette Graham, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Philipp Koehn, Varvara Logacheva, Christof Monz, Matteo Negri, Aur{\'e}lie N{\'e}v{\'e}ol, Mariana Neves, Martin Popel, Matt Post, Raphael Rubino, Carolina Scarton, Lucia Specia, Marco Turchi, Karin Verspoor, Marcos Zampieri
no code implementations • WS 2016 • Jan-Thorsten Peter, Tamer Alkhouli, Hermann Ney, Matthias Huck, Fabienne Braune, Alex Fraser, er, Ale{\v{s}} Tamchyna, Ond{\v{r}}ej Bojar, Barry Haddow, Rico Sennrich, Fr{\'e}d{\'e}ric Blain, Lucia Specia, Jan Niehues, Alex Waibel, Alex Allauzen, re, Lauriane Aufrant, Franck Burlot, Elena Knyazeva, Thomas Lavergne, Fran{\c{c}}ois Yvon, M{\=a}rcis Pinnis, Stella Frank
Ranked #12 on Machine Translation on WMT2016 English-Romanian
1 code implementation • EMNLP 2016 • Alexandra Birch, Omri Abend, Ondrej Bojar, Barry Haddow
Human evaluation of machine translation normally uses sentence-level measures such as relative ranking or adequacy scales.
1 code implementation • WS 2016 • Rico Sennrich, Barry Haddow
Neural machine translation has recently achieved impressive results, while using little in the way of external linguistic information.
Ranked #3 on Machine Translation on WMT2016 English-German
1 code implementation • WS 2016 • Rico Sennrich, Barry Haddow, Alexandra Birch
We participated in the WMT 2016 shared news translation task by building neural translation systems for four language pairs, each trained in both directions: English<->Czech, English<->German, English<->Romanian and English<->Russian.
Ranked #1 on Machine Translation on WMT2016 Czech-English
2 code implementations • ACL 2016 • Rico Sennrich, Barry Haddow, Alexandra Birch
Neural Machine Translation (NMT) has obtained state-of-the art performance for several language pairs, while only using parallel data for training.
no code implementations • WS 2015 • Ond{\v{r}}ej Bojar, Rajen Chatterjee, Christian Federmann, Barry Haddow, Matthias Huck, Chris Hokamp, Philipp Koehn, Varvara Logacheva, Christof Monz, Matteo Negri, Matt Post, Carolina Scarton, Lucia Specia, Marco Turchi
25 code implementations • ACL 2016 • Rico Sennrich, Barry Haddow, Alexandra Birch
Neural machine translation (NMT) models typically operate with a fixed vocabulary, but translation is an open-vocabulary problem.
Ranked #1 on Machine Translation on WMT2015 English-Russian
no code implementations • WS 2014 • Ondrej Bojar, Christian Buck, Christian Federmann, Barry Haddow, Philipp Koehn, Johannes Leveling, Christof Monz, Pavel Pecina, Matt Post, Herve Saint-Amand, Radu Soricut, Lucia Specia, Aleš Tamchyna