Search Results for author: Georgiana Dinu

Found 30 papers, 7 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

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

How Should Markup Tags Be Translated?

1 code implementation WMT (EMNLP) 2020 Greg Hanneman, Georgiana Dinu

The ability of machine translation (MT) models to correctly place markup is crucial to generating high-quality translations of formatted input.

Data Augmentation Machine Translation +2

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

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

Joint translation and unit conversion for end-to-end localization

no code implementations WS 2020 Georgiana Dinu, Prashant Mathur, Marcello Federico, Stanislas Lauly, Yaser Al-Onaizan

A variety of natural language tasks require processing of textual data which contains a mix of natural language and formal languages such as mathematical expressions.

Data Augmentation Translation

Toward Mention Detection Robustness with Recurrent Neural Networks

no code implementations24 Feb 2016 Thien Huu Nguyen, Avirup Sil, Georgiana Dinu, Radu Florian

One of the key challenges in natural language processing (NLP) is to yield good performance across application domains and languages.

named-entity-recognition Named Entity Recognition +2

From Visual Attributes to Adjectives through Decompositional Distributional Semantics

no code implementations TACL 2015 Angeliki Lazaridou, Georgiana Dinu, Adam Liska, Marco Baroni

By building on the recent "zero-shot learning" approach, and paying attention to the linguistic nature of attributes as noun modifiers, and specifically adjectives, we show that it is possible to tag images with attribute-denoting adjectives even when no training data containing the relevant annotation are available.

Attribute Object +4

Improving zero-shot learning by mitigating the hubness problem

4 code implementations20 Dec 2014 Georgiana Dinu, Angeliki Lazaridou, Marco Baroni

The zero-shot paradigm exploits vector-based word representations extracted from text corpora with unsupervised methods to learn general mapping functions from other feature spaces onto word space, where the words associated to the nearest neighbours of the mapped vectors are used as their linguistic labels.

Image Retrieval Retrieval +1

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