Search Results for author: Anna Currey

Found 18 papers, 6 papers with code

GFST: Gender-Filtered Self-Training for More Accurate Gender in Translation

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.

Machine Translation Translation

Distilling Multiple Domains for Neural Machine Translation

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.

Machine Translation Translation

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

Fine-Tuned Machine Translation Metrics Struggle in Unseen Domains

1 code implementation28 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.

Machine Translation Translation

RAMP: Retrieval and Attribute-Marking Enhanced Prompting for Attribute-Controlled Translation

no code implementations26 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.

Attribute Machine Translation +4

Pseudo-Label Training and Model Inertia in Neural Machine Translation

no code implementations19 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.

Knowledge Distillation Machine Translation +3

MT-GenEval: A Counterfactual and Contextual Dataset for Evaluating Gender Accuracy in Machine Translation

1 code implementation2 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.

counterfactual Ethics +3

CoCoA-MT: A Dataset and Benchmark for Contrastive Controlled MT with Application to Formality

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.

Machine Translation Sentence +2

Faithful Target Attribute Prediction in Neural Machine Translation

1 code implementation24 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.

Attribute Data Augmentation +4

Improving Gender Translation Accuracy with Filtered Self-Training

no code implementations15 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.

Machine Translation Sentence +1

Zero-Resource Neural Machine Translation with Monolingual Pivot Data

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.

Machine Translation NMT +1

Incorporating Source Syntax into Transformer-Based Neural Machine Translation

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.

Machine Translation NMT +1

Multi-Source Syntactic Neural Machine Translation

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.

Machine Translation Sentence +1

Unsupervised Source Hierarchies for Low-Resource Neural Machine Translation

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

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