Search Results for author: Xing Niu

Found 22 papers, 11 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

SpeechVerse: A Large-scale Generalizable Audio Language Model

no code implementations14 May 2024 Nilaksh Das, Saket Dingliwal, Srikanth Ronanki, Rohit Paturi, David Huang, Prashant Mathur, Jie Yuan, Dhanush Bekal, Xing Niu, Sai Muralidhar Jayanthi, Xilai Li, Karel Mundnich, Monica Sunkara, Sundararajan Srinivasan, Kyu J Han, Katrin Kirchhoff

The models are instruction finetuned using continuous latent representations extracted from the speech foundation model to achieve optimal zero-shot performance on a diverse range of speech processing tasks using natural language instructions.

Automatic Speech Recognition Benchmarking +4

End-to-End Single-Channel Speaker-Turn Aware Conversational Speech Translation

1 code implementation1 Nov 2023 Juan Zuluaga-Gomez, Zhaocheng Huang, Xing Niu, Rohit Paturi, Sundararajan Srinivasan, Prashant Mathur, Brian Thompson, Marcello Federico

Conventional speech-to-text translation (ST) systems are trained on single-speaker utterances, and they may not generalize to real-life scenarios where the audio contains conversations by multiple speakers.

Automatic Speech Recognition speech-recognition +3

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

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 Sentence +1

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 NMT +1

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 NMT +1

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 +3

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 +3

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