Search Results for author: Barry Haddow

Found 73 papers, 15 papers with code

Findings of the IWSLT 2022 Evaluation Campaign

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.

Speech-to-Speech Translation Translation

The Samsung and University of Edinburgh’s submission to IWSLT17

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.

Domain Adaptation Translation

Samsung and University of Edinburgh’s System for the IWSLT 2018 Low Resource MT Task

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.

Findings of the 2021 Conference on Machine Translation (WMT21)

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.

Machine Translation Translation

Revisiting End-to-End Speech-to-Text Translation From Scratch

1 code implementation9 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.

Speech Recognition Speech-to-Text Translation +1

Non-Autoregressive Machine Translation: It's Not as Fast as it Seems

no code implementations4 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.

Machine Translation Translation

The ELITR ECA Corpus

no code implementations15 Sep 2021 Philip Williams, Barry Haddow

We present the ELITR ECA corpus, a multilingual corpus derived from publications of the European Court of Auditors.


Beyond Sentence-Level End-to-End Speech Translation: Context Helps

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.

feature selection Machine Translation +1

Exploring Unsupervised Pretraining Objectives for Machine Translation

no code implementations 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.

Language Modelling Machine Translation +2

SLTEV: Comprehensive Evaluation of Spoken Language Translation

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.

Machine Translation Translation

Adaptive Feature Selection for End-to-End Speech 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.

Data Augmentation feature selection +1

Dynamic Masking for Improved Stability in Spoken Language Translation

no code implementations30 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 Machine Translation +1

Bridging Linguistic Typology and Multilingual Machine Translation with Multi-View Language Representations

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.

Machine Translation Translation

PMIndia -- A Collection of Parallel Corpora of Languages of India

2 code implementations27 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.

Machine Translation Multilingual NLP +1

Translationese in Machine Translation Evaluation

no code implementations24 Jun 2019 Yvette Graham, Barry Haddow, Philipp Koehn

Finally, we provide a comprehensive check-list for future machine translation evaluation.

Machine Translation Translation

Evaluating Discourse Phenomena in Neural Machine Translation

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%).

Machine Translation Translation

Regularization techniques for fine-tuning in neural machine translation

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.

Domain Adaptation L2 Regularization +3

Practical 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.

Machine Translation Translation

HUME: Human UCCA-Based Evaluation of Machine Translation

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.

Machine Translation Translation

Linguistic Input Features Improve Neural Machine Translation

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.

Machine Translation Translation

Edinburgh Neural Machine Translation Systems for WMT 16

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.

Machine Translation Translation

Neural Machine Translation of Rare Words with Subword Units

24 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.


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