Publications

Document-Level Machine Translation with Large-Scale Public Parallel Corpora

Proyag Pal, Alexandra Birch, and Kenneth Heafield.
Published at ACL 2024.
[PDF] [ACL Anthology] [Poster] [BibTeX]

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Despite the fact that document-level machine translation has inherent advantages over sentence-level machine translation due to additional information available to a model from document context, most translation systems continue to operate at a sentence level. This is primarily due to the severe lack of publicly available large-scale parallel corpora at the document level. We release a large-scale open parallel corpus with document context extracted from ParaCrawl in five language pairs, along with code to compile document-level datasets for any language pair supported by ParaCrawl. We train context-aware models on these datasets and find improvements in terms of overall translation quality and targeted document-level phenomena. We also analyse how much long-range information is useful to model some of these discourse phenomena and find models are able to utilise context from several preceding sentences.

Improving Isochronous Machine Translation with Target Factors and Auxiliary Counters

Proyag Pal, Brian Thompson, Yogesh Virkar, Prashant Mathur, Alexandra Chronopoulou, and Marcello Federico.
Published at INTERSPEECH 2023.
[PDF] [ISCA Archive] [arXiv] [BibTeX]

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To translate speech for automatic dubbing, machine translation needs to be isochronous, i.e. translated speech needs to be aligned with the source in terms of speech durations. We introduce target factors in a transformer model to predict durations jointly with target language phoneme sequences. We also introduce auxiliary counters to help the decoder to keep track of the timing information while generating target phonemes. We show that our model improves translation quality and isochrony compared to previous work where the translation model is instead trained to predict interleaved sequences of phonemes and durations.

Findings of the IWSLT 2023 Evaluation Campaign

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, and Rodolfo Zevallos.
Published at IWSLT 2023.
[PDF] [ACL Anthology] [BibTeX]

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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.

Cheating to Identify Hard Problems for Neural Machine Translation

Proyag Pal and Kenneth Heafield.
Published at EACL (Findings) 2023.
[PDF] [ACL Anthology] [BibTeX]

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We identify hard problems for neural machine translation models by analyzing progressively higher-scoring translations generated by letting models cheat to various degrees. If a system cheats and still gets something wrong, that suggests it is a hard problem. We experiment with two forms of cheating: providing the model a compressed representation of the target as an additional input, and fine-tuning on the test set. Contrary to popular belief, we find that the most frequent tokens are not necessarily the most accurately translated due to these often being function words and punctuation that can be used more flexibly in translation, or content words which can easily be paraphrased. We systematically analyze system outputs to identify categories of tokens which are particularly hard for the model to translate, and find that this includes certain types of named entities, subordinating conjunctions, and unknown and foreign words. We also encounter a phenomenon where words, often names, which were not infrequent in the training data are still repeatedly mistranslated by the models — we dub this the Fleetwood Mac problem.

Cheat Codes to Quantify Missing Source Information in Neural Machine Translation

Proyag Pal and Kenneth Heafield.
Published at NAACL 2022.
[PDF] [ACL Anthology] [Poster] [BibTeX]

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This paper describes a method to quantify the amount of information H(t|s) added by the target sentence t that is not present in the source s in a neural machine translation system. We do this by providing the model the target sentence in a highly compressed form (a "cheat code"), and exploring the effect of the size of the cheat code. We find that the model is able to capture extra information from just a single float representation of the target and nearly reproduces the target with two 32-bit floats per target token.

The University of Edinburgh’s Bengali-Hindi Submissions to the WMT21 News Translation Task

Proyag Pal, Alham Fikri Aji, Pinzhen Chen, and Sukanta Sen.
Published at WMT21 at EMNLP 2021.
[PDF] [ACL Anthology] [Poster] [BibTeX]

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We describe the University of Edinburgh’s Bengali↔Hindi constrained systems submitted to the WMT21 News Translation task. We submitted ensembles of Transformer models built with large-scale back-translation and fine-tuned on subsets of training data retrieved based on similarity to the target domain. For both translation directions, our submissions are among the best-performing constrained systems according to human evaluation.