Search Results for author: Xing Niu

Found 16 papers, 8 papers with code

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

The UMD Machine Translation Systems at IWSLT 2016: English-to-French Translation of Speech Transcripts

no code implementations IWSLT 2016 Xing Niu, Marine Carpuat

We describe the University of Maryland machine translation system submitted to the IWSLT 2016 Microsoft Speech Language Translation (MSLT) English-French task.

Machine Translation Translation

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

1 code implementation9 May 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 Translation

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.

Data Augmentation Gender Prediction +2

Dual Reconstruction: a Unifying Objective for Semi-Supervised Neural Machine Translation

no code implementations Findings of the Association for Computational Linguistics 2020 Weijia Xu, Xing Niu, Marine Carpuat

While Iterative Back-Translation and Dual Learning effectively incorporate monolingual training data in neural machine translation, they use different objectives and heuristic gradient approximation strategies, and have not been extensively compared.

Machine Translation Translation

Controlling Neural Machine Translation Formality with Synthetic Supervision

1 code implementation20 Nov 2019 Xing Niu, Marine Carpuat

This work aims to produce translations that convey source language content at a formality level that is appropriate for a particular audience.

Machine Translation Translation

Differentiable Sampling with Flexible Reference Word Order for Neural Machine Translation

1 code implementation NAACL 2019 Weijia Xu, Xing Niu, Marine Carpuat

Despite some empirical success at correcting exposure bias in machine translation, scheduled sampling algorithms suffer from a major drawback: they incorrectly assume that words in the reference translations and in sampled sequences are aligned at each time step.

Machine Translation Translation

Bi-Directional Differentiable Input Reconstruction for Low-Resource Neural Machine Translation

1 code implementation NAACL 2019 Xing Niu, Weijia Xu, Marine Carpuat

We aim to better exploit the limited amounts of parallel text available in low-resource settings by introducing a differentiable reconstruction loss for neural machine translation (NMT).

Low-Resource Neural Machine Translation Translation

Bi-Directional Neural Machine Translation with Synthetic Parallel Data

no code implementations WS 2018 Xing Niu, Michael Denkowski, Marine Carpuat

Despite impressive progress in high-resource settings, Neural Machine Translation (NMT) still struggles in low-resource and out-of-domain scenarios, often failing to match the quality of phrase-based translation.

Machine Translation Translation

Identifying Semantic Divergences in Parallel Text without Annotations

1 code implementation NAACL 2018 Yogarshi Vyas, Xing Niu, Marine Carpuat

Recognizing that even correct translations are not always semantically equivalent, we automatically detect meaning divergences in parallel sentence pairs with a deep neural model of bilingual semantic similarity which can be trained for any parallel corpus without any manual annotation.

Machine Translation Semantic Similarity +2

A Study of Style in Machine Translation: Controlling the Formality of Machine Translation Output

no code implementations EMNLP 2017 Xing Niu, Marianna Martindale, Marine Carpuat

Stylistic variations of language, such as formality, carry speakers{'} intention beyond literal meaning and should be conveyed adequately in translation.

Domain Adaptation Machine Translation +1

Detecting Cross-Lingual Semantic Divergence for Neural Machine Translation

no code implementations WS 2017 Marine Carpuat, Yogarshi Vyas, Xing Niu

Parallel corpora are often not as parallel as one might assume: non-literal translations and noisy translations abound, even in curated corpora routinely used for training and evaluation.

Domain Adaptation Machine Translation +2

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