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of 24
pro vyhledávání: '"Currey, Anna"'
We introduce a new, extensive multidimensional quality metrics (MQM) annotated dataset covering 11 language pairs in the biomedical domain. We use this dataset to investigate whether machine translation (MT) metrics which are fine-tuned on human-gene
Externí odkaz:
http://arxiv.org/abs/2402.18747
Autor:
Sarti, Gabriele, Htut, Phu Mon, Niu, Xing, Hsu, Benjamin, Currey, Anna, Dinu, Georgiana, Nadejde, Maria
Publikováno v:
Proceedings of ACL (2023) 1476-1490
Attribute-controlled translation (ACT) is a subtask of machine translation that involves controlling stylistic or linguistic attributes (like formality and gender) of translation outputs. While ACT has garnered attention in recent years due to its us
Externí odkaz:
http://arxiv.org/abs/2305.17131
Like many other machine learning applications, neural machine translation (NMT) benefits from over-parameterized deep neural models. However, these models have been observed to be brittle: NMT model predictions are sensitive to small input changes an
Externí odkaz:
http://arxiv.org/abs/2305.11808
Autor:
Currey, Anna, Nădejde, Maria, Pappagari, Raghavendra, Mayer, Mia, Lauly, Stanislas, Niu, Xing, Hsu, Benjamin, Dinu, Georgiana
As generic machine translation (MT) quality has improved, the need for targeted benchmarks that explore fine-grained aspects of quality has increased. In particular, gender accuracy in translation can have implications in terms of output fluency, tra
Externí odkaz:
http://arxiv.org/abs/2211.01355
Autor:
Hieber, Felix, Denkowski, Michael, Domhan, Tobias, Barros, Barbara Darques, Ye, Celina Dong, Niu, Xing, Hoang, Cuong, Tran, Ke, Hsu, Benjamin, Nadejde, Maria, Lakew, Surafel, Mathur, Prashant, Currey, Anna, Federico, Marcello
Sockeye 3 is the latest version of the Sockeye toolkit for Neural Machine Translation (NMT). Now based on PyTorch, Sockeye 3 provides faster model implementations and more advanced features with a further streamlined codebase. This enables broader ex
Externí odkaz:
http://arxiv.org/abs/2207.05851
The machine translation (MT) task is typically formulated as that of returning a single translation for an input segment. However, in many cases, multiple different translations are valid and the appropriate translation may depend on the intended tar
Externí odkaz:
http://arxiv.org/abs/2205.04022
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. We argue that predicting the target word and attributes simultaneously is an effective way
Externí odkaz:
http://arxiv.org/abs/2109.12105
Targeted evaluations have found that machine translation systems often output incorrect gender, even when the gender is clear from context. Furthermore, these incorrectly gendered translations have the potential to reflect or amplify social biases. W
Externí odkaz:
http://arxiv.org/abs/2104.07695
Autor:
Currey, Anna, Heafield, Kenneth
We introduce a novel multi-source technique for incorporating source syntax into neural machine translation using linearized parses. This is achieved by employing separate encoders for the sequential and parsed versions of the same source sentence; t
Externí odkaz:
http://arxiv.org/abs/1808.10267
Autor:
Sennrich, Rico, Birch, Alexandra, Currey, Anna, Germann, Ulrich, Haddow, Barry, Heafield, Kenneth, Barone, Antonio Valerio Miceli, Williams, Philip
This paper describes the University of Edinburgh's submissions to the WMT17 shared news translation and biomedical translation tasks. We participated in 12 translation directions for news, translating between English and Czech, German, Latvian, Russi
Externí odkaz:
http://arxiv.org/abs/1708.00726