1 code implementation • EMNLP 2021 • Prafulla Kumar Choubey, Anna Currey, Prashant Mathur, Georgiana Dinu
Targeted evaluations have found that machine translation systems often output incorrect gender in translations, even when the gender is clear from context.
no code implementations • EMNLP 2020 • Anna Currey, Prashant Mathur, Georgiana Dinu
Neural machine translation achieves impressive results in high-resource conditions, but performance often suffers when the input domain is low-resource.
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.
1 code implementation • 28 Feb 2024 • Vilém Zouhar, Shuoyang Ding, Anna Currey, Tatyana Badeka, Jenyuan Wang, Brian Thompson
We introduce a new, extensive multidimensional quality metrics (MQM) annotated dataset covering 11 language pairs in the biomedical domain.
no code implementations • 26 May 2023 • Gabriele Sarti, Phu Mon Htut, Xing Niu, Benjamin Hsu, Anna Currey, Georgiana Dinu, Maria Nadejde
Attribute-controlled translation (ACT) is a subtask of machine translation that involves controlling stylistic or linguistic attributes (like formality and gender) of translation outputs.
no code implementations • 19 May 2023 • Benjamin Hsu, Anna Currey, Xing Niu, Maria Nădejde, Georgiana Dinu
While the effect of PLT on quality is well-documented, we highlight a lesser-known effect: PLT can enhance a model's stability to model updates and input perturbations, a set of properties we call model inertia.
1 code implementation • 2 Nov 2022 • Anna Currey, Maria Nădejde, Raghavendra Pappagari, Mia Mayer, Stanislas Lauly, Xing Niu, Benjamin Hsu, Georgiana Dinu
As generic machine translation (MT) quality has improved, the need for targeted benchmarks that explore fine-grained aspects of quality has increased.
2 code implementations • 12 Jul 2022 • Felix Hieber, Michael Denkowski, Tobias Domhan, Barbara Darques Barros, Celina Dong Ye, Xing Niu, Cuong Hoang, Ke Tran, Benjamin Hsu, Maria Nadejde, Surafel Lakew, Prashant Mathur, Anna Currey, Marcello Federico
When running comparable models, Sockeye 3 is up to 126% faster than other PyTorch implementations on GPUs and up to 292% faster on CPUs.
2 code implementations • Findings (NAACL) 2022 • Maria Nădejde, Anna Currey, Benjamin Hsu, Xing Niu, Marcello Federico, Georgiana Dinu
However, in many cases, multiple different translations are valid and the appropriate translation may depend on the intended target audience, characteristics of the speaker, or even the relationship between speakers.
1 code implementation • 24 Sep 2021 • Xing Niu, Georgiana Dinu, Prashant Mathur, Anna Currey
The training data used in NMT is rarely controlled with respect to specific attributes, such as word casing or gender, which can cause errors in translations.
no code implementations • 15 Apr 2021 • Prafulla Kumar Choubey, Anna Currey, Prashant Mathur, Georgiana Dinu
Targeted evaluations have found that machine translation systems often output incorrect gender, even when the gender is clear from context.
no code implementations • WS 2019 • Anna Currey, Kenneth Heafield
An extension to zero-shot NMT is zero-resource NMT, which generates pseudo-parallel corpora using a zero-shot system and further trains the zero-shot system on that data.
no code implementations • WS 2019 • Anna Currey, Kenneth Heafield
Transformer-based neural machine translation (NMT) has recently achieved state-of-the-art performance on many machine translation tasks.
no code implementations • EMNLP 2018 • Anna Currey, Kenneth Heafield
We introduce a novel multi-source technique for incorporating source syntax into neural machine translation using linearized parses.
no code implementations • WS 2018 • Anna Currey, Kenneth Heafield
Incorporating source syntactic information into neural machine translation (NMT) has recently proven successful (Eriguchi et al., 2016; Luong et al., 2016).
Low-Resource Neural Machine Translation Natural Language Inference +4
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.