Xing Niu


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Milind Agarwal | Sweta Agrawal | Antonios Anastasopoulos | Luisa Bentivogli | Ondřej Bojar | Claudia Borg | Marine Carpuat | Roldano Cattoni | Mauro Cettolo | Mingda Chen | William Chen | Khalid Choukri | Alexandra Chronopoulou | Anna Currey | Thierry Declerck | Qianqian Dong | Kevin Duh | Yannick Estève | Marcello Federico | Souhir Gahbiche | Barry Haddow | Benjamin Hsu | Phu Mon Htut | Hirofumi Inaguma | Dávid Javorský | John Judge | Yasumasa Kano | Tom Ko | Rishu Kumar | Pengwei Li | Xutai Ma | Prashant Mathur | Evgeny Matusov | Paul McNamee | John P. McCrae | Kenton Murray | Maria Nadejde | Satoshi Nakamura | Matteo Negri | Ha Nguyen | Jan Niehues | Xing Niu | Atul Kr. Ojha | John E. Ortega | Proyag Pal | Juan Pino | Lonneke van der Plas | Peter Polák | Elijah Rippeth | Elizabeth Salesky | Jiatong Shi | Matthias Sperber | Sebastian Stüker | Katsuhito Sudoh | Yun Tang | Brian Thompson | Kevin Tran | Marco Turchi | Alex Waibel | Mingxuan Wang | Shinji Watanabe | Rodolfo Zevallos
Proceedings of the 20th International Conference on Spoken Language Translation (IWSLT 2023)

This paper reports on the shared tasks organized by the 20th IWSLT Conference. The shared tasks address 9 scientific challenges in spoken language translation: simultaneous and offline translation, automatic subtitling and dubbing, speech-to-speech translation, multilingual, dialect and low-resource speech translation, and formality control. The shared tasks attracted a total of 38 submissions by 31 teams. The growing interest towards spoken language translation is also witnessed by the constantly increasing number of shared task organizers and contributors to the overview paper, almost evenly distributed across industry and academia.

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RAMP: Retrieval and Attribute-Marking Enhanced Prompting for Attribute-Controlled Translation
Gabriele Sarti | Phu Mon Htut | Xing Niu | Benjamin Hsu | Anna Currey | Georgiana Dinu | Maria Nadejde
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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 usefulness in real-world applications, progress in the task is currently limited by dataset availability, since most prior approaches rely on supervised methods. To address this limitation, we propose Retrieval and Attribute-Marking enhanced Prompting (RAMP), which leverages large multilingual language models to perform ACT in few-shot and zero-shot settings. RAMP improves generation accuracy over the standard prompting approach by (1) incorporating a semantic similarity retrieval component for selecting similar in-context examples, and (2) marking in-context examples with attribute annotations. Our comprehensive experiments show that RAMP is a viable approach in both zero-shot and few-shot settings.


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Findings of the IWSLT 2022 Evaluation Campaign
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
Proceedings of the 19th International Conference on Spoken Language Translation (IWSLT 2022)

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. A total of 27 teams participated in at least one of the shared tasks. This paper details, for each shared task, the purpose of the task, the data that were released, the evaluation metrics that were applied, the submissions that were received and the results that were achieved.

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CoCoA-MT: A Dataset and Benchmark for Contrastive Controlled MT with Application to Formality
Maria Nadejde | Anna Currey | Benjamin Hsu | Xing Niu | Marcello Federico | Georgiana Dinu
Findings of the Association for Computational Linguistics: NAACL 2022

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 target audience, characteristics of the speaker, or even the relationship between speakers. Specific problems arise when dealing with honorifics, particularly translating from English into languages with formality markers. For example, the sentence “Are you sure?” can be translated in German as “Sind Sie sich sicher?” (formal register) or “Bist du dir sicher?” (informal). Using wrong or inconsistent tone may be perceived as inappropriate or jarring for users of certain cultures and demographics. This work addresses the problem of learning to control target language attributes, in this case formality, from a small amount of labeled contrastive data. We introduce an annotated dataset (CoCoA-MT) and an associated evaluation metric for training and evaluating formality-controlled MT models for six diverse target languages. We show that we can train formality-controlled models by fine-tuning on labeled contrastive data, achieving high accuracy (82% in-domain and 73% out-of-domain) while maintaining overall quality.

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MT-GenEval: A Counterfactual and Contextual Dataset for Evaluating Gender Accuracy in Machine Translation
Anna Currey | Maria Nadejde | Raghavendra Reddy Pappagari | Mia Mayer | Stanislas Lauly | Xing Niu | Benjamin Hsu | Georgiana Dinu
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

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, translation accuracy, and ethics. In this paper, we introduce MT-GenEval, a benchmark for evaluating gender accuracy in translation from English into eight widely-spoken languages. MT-GenEval complements existing benchmarks by providing realistic, gender-balanced, counterfactual data in eight language pairs where the gender of individuals is unambiguous in the input segment, including multi-sentence segments requiring inter-sentential gender agreement. Our data and code is publicly available under a CC BY SA 3.0 license.


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Evaluating Robustness to Input Perturbations for Neural Machine Translation
Xing Niu | Prashant Mathur | Georgiana Dinu | Yaser Al-Onaizan
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Neural Machine Translation (NMT) models are sensitive to small perturbations in the input. Robustness to such perturbations is typically measured using translation quality metrics such as BLEU on the noisy input. This paper proposes additional metrics which measure the relative degradation and changes in translation when small perturbations are added to the input. We focus on a class of models employing subword regularization to address robustness and perform extensive evaluations of these models using the robustness measures proposed. Results show that our proposed metrics reveal a clear trend of improved robustness to perturbations when subword regularization methods are used.

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Dual Reconstruction: a Unifying Objective for Semi-Supervised Neural Machine Translation
Weijia Xu | Xing Niu | Marine Carpuat
Findings of the Association for Computational Linguistics: EMNLP 2020

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. We introduce a novel dual reconstruction objective that provides a unified view of Iterative Back-Translation and Dual Learning. It motivates a theoretical analysis and controlled empirical study on German-English and Turkish-English tasks, which both suggest that Iterative Back-Translation is more effective than Dual Learning despite its relative simplicity.

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The Sockeye 2 Neural Machine Translation Toolkit at AMTA 2020
Tobias Domhan | Michael Denkowski | David Vilar | Xing Niu | Felix Hieber | Kenneth Heafield
Proceedings of the 14th Conference of the Association for Machine Translation in the Americas (Volume 1: Research Track)


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Bi-Directional Differentiable Input Reconstruction for Low-Resource Neural Machine Translation
Xing Niu | Weijia Xu | Marine Carpuat
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

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). This loss compares original inputs to reconstructed inputs, obtained by back-translating translation hypotheses into the input language. We leverage differentiable sampling and bi-directional NMT to train models end-to-end, without introducing additional parameters. This approach achieves small but consistent BLEU improvements on four language pairs in both translation directions, and outperforms an alternative differentiable reconstruction strategy based on hidden states.

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Differentiable Sampling with Flexible Reference Word Order for Neural Machine Translation
Weijia Xu | Xing Niu | Marine Carpuat
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

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. Our new differentiable sampling algorithm addresses this issue by optimizing the probability that the reference can be aligned with the sampled output, based on a soft alignment predicted by the model itself. As a result, the output distribution at each time step is evaluated with respect to the whole predicted sequence. Experiments on IWSLT translation tasks show that our approach improves BLEU compared to maximum likelihood and scheduled sampling baselines. In addition, our approach is simpler to train with no need for sampling schedule and yields models that achieve larger improvements with smaller beam sizes.


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Identifying Semantic Divergences in Parallel Text without Annotations
Yogarshi Vyas | Xing Niu | Marine Carpuat
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

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. We show that our semantic model detects divergences more accurately than models based on surface features derived from word alignments, and that these divergences matter for neural machine translation.

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Bi-Directional Neural Machine Translation with Synthetic Parallel Data
Xing Niu | Michael Denkowski | Marine Carpuat
Proceedings of the 2nd Workshop on Neural Machine Translation and Generation

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. We propose a novel technique that combines back-translation and multilingual NMT to improve performance in these difficult cases. Our technique trains a single model for both directions of a language pair, allowing us to back-translate source or target monolingual data without requiring an auxiliary model. We then continue training on the augmented parallel data, enabling a cycle of improvement for a single model that can incorporate any source, target, or parallel data to improve both translation directions. As a byproduct, these models can reduce training and deployment costs significantly compared to uni-directional models. Extensive experiments show that our technique outperforms standard back-translation in low-resource scenarios, improves quality on cross-domain tasks, and effectively reduces costs across the board.

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Multi-Task Neural Models for Translating Between Styles Within and Across Languages
Xing Niu | Sudha Rao | Marine Carpuat
Proceedings of the 27th International Conference on Computational Linguistics

Generating natural language requires conveying content in an appropriate style. We explore two related tasks on generating text of varying formality: monolingual formality transfer and formality-sensitive machine translation. We propose to solve these tasks jointly using multi-task learning, and show that our models achieve state-of-the-art performance for formality transfer and are able to perform formality-sensitive translation without being explicitly trained on style-annotated translation examples.


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A Study of Style in Machine Translation: Controlling the Formality of Machine Translation Output
Xing Niu | Marianna Martindale | Marine Carpuat
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

Stylistic variations of language, such as formality, carry speakers’ intention beyond literal meaning and should be conveyed adequately in translation. We propose to use lexical formality models to control the formality level of machine translation output. We demonstrate the effectiveness of our approach in empirical evaluations, as measured by automatic metrics and human assessments.

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Detecting Cross-Lingual Semantic Divergence for Neural Machine Translation
Marine Carpuat | Yogarshi Vyas | Xing Niu
Proceedings of the First Workshop on Neural Machine Translation

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. We use a cross-lingual textual entailment system to distinguish sentence pairs that are parallel in meaning from those that are not, and show that filtering out divergent examples from training improves translation quality.

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Discovering Stylistic Variations in Distributional Vector Space Models via Lexical Paraphrases
Xing Niu | Marine Carpuat
Proceedings of the Workshop on Stylistic Variation

Detecting and analyzing stylistic variation in language is relevant to diverse Natural Language Processing applications. In this work, we investigate whether salient dimensions of style variations are embedded in standard distributional vector spaces of word meaning. We hypothesizes that distances between embeddings of lexical paraphrases can help isolate style from meaning variations and help identify latent style dimensions. We conduct a qualitative analysis of latent style dimensions, and show the effectiveness of identified style subspaces on a lexical formality prediction task.


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The UMD Machine Translation Systems at IWSLT 2016: English-to-French Translation of Speech Transcripts
Xing Niu | Marine Carpuat
Proceedings of the 13th International Conference on Spoken Language Translation

We describe the University of Maryland machine translation system submitted to the IWSLT 2016 Microsoft Speech Language Translation (MSLT) English-French task. Our main finding is that translating conversation transcripts turned out to not be as challenging as we expected: while translation quality is of course not perfect, a straightforward phrase-based system trained on movie subtitles yields high BLEU scores (high 40s on the development set) and manual analysis of 100 examples showed that 61 of them were correctly translated, and errors were mostly local disfluencies in the remaining examples.