Prashant Mathur


2022

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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.

2021

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GFST: Gender-Filtered Self-Training for More Accurate Gender in Translation
Prafulla Kumar Choubey | Anna Currey | Prashant Mathur | Georgiana Dinu
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Targeted evaluations have found that machine translation systems often output incorrect gender in translations, even when the gender is clear from context. Furthermore, these incorrectly gendered translations have the potential to reflect or amplify social biases. We propose gender-filtered self-training (GFST) to improve gender translation accuracy on unambiguously gendered inputs. Our GFST approach uses a source monolingual corpus and an initial model to generate gender-specific pseudo-parallel corpora which are then filtered and added to the training data. We evaluate GFST on translation from English into five languages, finding that it improves gender accuracy without damaging generic quality. We also show the viability of GFST on several experimental settings, including re-training from scratch, fine-tuning, controlling the gender balance of the data, forward translation, and back-translation.

2020

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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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Joint Translation and Unit Conversion for End-to-end Localization
Georgiana Dinu | Prashant Mathur | Marcello Federico | Stanislas Lauly | Yaser Al-Onaizan
Proceedings of the 17th International Conference on Spoken Language Translation

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. In this paper, we take unit conversions as an example and propose a data augmentation technique which lead to models learning both translation and conversion tasks as well as how to adequately switch between them for end-to-end localization.

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Distilling Multiple Domains for Neural Machine Translation
Anna Currey | Prashant Mathur | Georgiana Dinu
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Neural machine translation achieves impressive results in high-resource conditions, but performance often suffers when the input domain is low-resource. The standard practice of adapting a separate model for each domain of interest does not scale well in practice from both a quality perspective (brittleness under domain shift) as well as a cost perspective (added maintenance and inference complexity). In this paper, we propose a framework for training a single multi-domain neural machine translation model that is able to translate several domains without increasing inference time or memory usage. We show that this model can improve translation on both high- and low-resource domains over strong multi-domain baselines. In addition, our proposed model is effective when domain labels are unknown during training, as well as robust under noisy data conditions.

2019

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Training Neural Machine Translation to Apply Terminology Constraints
Georgiana Dinu | Prashant Mathur | Marcello Federico | Yaser Al-Onaizan
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

This paper proposes a novel method to inject custom terminology into neural machine translation at run time. Previous works have mainly proposed modifications to the decoding algorithm in order to constrain the output to include run-time-provided target terms. While being effective, these constrained decoding methods add, however, significant computational overhead to the inference step, and, as we show in this paper, can be brittle when tested in realistic conditions. In this paper we approach the problem by training a neural MT system to learn how to use custom terminology when provided with the input. Comparative experiments show that our method is not only more effective than a state-of-the-art implementation of constrained decoding, but is also as fast as constraint-free decoding.

2018

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Generating E-Commerce Product Titles and Predicting their Quality
José G. Camargo de Souza | Michael Kozielski | Prashant Mathur | Ernie Chang | Marco Guerini | Matteo Negri | Marco Turchi | Evgeny Matusov
Proceedings of the 11th International Conference on Natural Language Generation

E-commerce platforms present products using titles that summarize product information. These titles cannot be created by hand, therefore an algorithmic solution is required. The task of automatically generating these titles given noisy user provided titles is one way to achieve the goal. The setting requires the generation process to be fast and the generated title to be both human-readable and concise. Furthermore, we need to understand if such generated titles are usable. As such, we propose approaches that (i) automatically generate product titles, (ii) predict their quality. Our approach scales to millions of products and both automatic and human evaluations performed on real-world data indicate our approaches are effective and applicable to existing e-commerce scenarios.

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Multi-lingual neural title generation for e-Commerce browse pages
Prashant Mathur | Nicola Ueffing | Gregor Leusch
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 3 (Industry Papers)

To provide better access of the inventory to buyers and better search engine optimization, e-Commerce websites are automatically generating millions of browse pages. A browse page consists of a set of slot name/value pairs within a given category, grouping multiple items which share some characteristics. These browse pages require a title describing the content of the page. Since the number of browse pages are huge, manual creation of these titles is infeasible. Previous statistical and neural approaches depend heavily on the availability of large amounts of data in a language. In this research, we apply sequence-to-sequence models to generate titles for high-resource as well as low-resource languages by leveraging transfer learning. We train these models on multi-lingual data, thereby creating one joint model which can generate titles in various different languages. Performance of the title generation system is evaluated on three different languages; English, German, and French, with a particular focus on low-resourced French language.

2017

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Generating titles for millions of browse pages on an e-Commerce site
Prashant Mathur | Nicola Ueffing | Gregor Leusch
Proceedings of the 10th International Conference on Natural Language Generation

We present two approaches to generate titles for browse pages in five different languages, namely English, German, French, Italian and Spanish. These browse pages are structured search pages in an e-commerce domain. We first present a rule-based approach to generate these browse page titles. In addition, we also present a hybrid approach which uses a phrase-based statistical machine translation engine on top of the rule-based system to assemble the best title. For the two languages English and German we have access to a large amount of already available rule-based generated and curated titles. For these languages we present an automatic post-editing approach which learns how to post-edit the rule-based titles into curated titles.

2015

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Topic adaptation for machine translation of e-commerce content
Prashant Mathur | Marcello Federico | Selçuk Köprü | Sharam Khadivi | Hassan Sawaf
Proceedings of Machine Translation Summit XV: Papers

2014

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Fast Domain Adaptation of SMT models without in-Domain Parallel Data
Prashant Mathur | Sriram Venkatapathy | Nicola Cancedda
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers

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Online multi-user adaptive statistical machine translation
Prashant Mathur | Mauro Cettolo | Marcello Federico | José G.C. de Souza
Proceedings of the 11th Conference of the Association for Machine Translation in the Americas: MT Researchers Track

In this paper we investigate the problem of adapting a machine translation system to the feedback provided by multiple post-editors. It is well know that translators might have very different post-editing styles and that this variability hinders the application of online learning methods, which indeed assume a homogeneous source of adaptation data. We hence propose multi-task learning to leverage bias information from each single post-editors in order to constrain the evolution of the SMT system. A new framework for significance testing with sentence level metrics is described which shows that Multi-Task learning approaches outperforms existing online learning approaches, with significant gains of 1.24 and 1.88 TER score over a strong online adaptive baseline, on a test set of post-edits produced by four translators texts and on a popular benchmark with multiple references, respectively.

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Optimized MT online learning in computer assisted translation
Prashant Mathur | Mauro Cettolo
Workshop on interactive and adaptive machine translation

In this paper we propose a cascading framework for optimizing online learning in machine translation for a computer assisted translation scenario. With the use of online learning, several hyperparameters associated with the learning algorithm are introduced. The number of iterations of online learning can affect the translation quality as well. We discuss these issues and propose a few approaches to optimize the hyperparameters and to find the number of iterations required for online learning. We experimentally show that optimizing hyperparameters and number of iterations in online learning yields consistent improvement against baseline results.

2013

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Online Learning Approaches in Computer Assisted Translation
Prashant Mathur | Mauro Cettolo | Marcello Federico
Proceedings of the Eighth Workshop on Statistical Machine Translation

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FBK’s machine translation systems for the IWSLT 2013 evaluation campaign
Nicola Bertoldi | M. Amin Farajian | Prashant Mathur | Nicholas Ruiz | Marcello Federico
Proceedings of the 10th International Workshop on Spoken Language Translation: Evaluation Campaign

This paper describes the systems submitted by FBK for the MT track of IWSLT 2013. We participated in the English-French as well as the bidirectional Persian-English translation tasks. We report substantial improvements in our English-French systems over last year’s baselines, largely due to improved techniques of combining translation and language models. For our Persian-English and English-Persian systems, we observe substantive improvements over baselines submitted by the workshop organizers, due to enhanced language-specific text normalization and the creation of a large monolingual news corpus in Persian.

2011

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The Uppsala-FBK systems at WMT 2011
Christian Hardmeier | Jörg Tiedemann | Markus Saers | Marcello Federico | Prashant Mathur
Proceedings of the Sixth Workshop on Statistical Machine Translation

2010

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Syntactic Construct : An Aid for translating English Nominal Compound into Hindi
Soma Paul | Prashant Mathur | Sushant Kishore
Proceedings of the NAACL HLT Workshop on Extracting and Using Constructions in Computational Linguistics