Search Results for author: Prashant Mathur

Found 25 papers, 9 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

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

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

SpeechVerse: A Large-scale Generalizable Audio Language Model

no code implementations14 May 2024 Nilaksh Das, Saket Dingliwal, Srikanth Ronanki, Rohit Paturi, Zhaocheng 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

PEAVS: Perceptual Evaluation of Audio-Visual Synchrony Grounded in Viewers' Opinion Scores

1 code implementation10 Apr 2024 Lucas Goncalves, Prashant Mathur, Chandrashekhar Lavania, Metehan Cekic, Marcello Federico, Kyu J. Han

Recent advancements in audio-visual generative modeling have been propelled by progress in deep learning and the availability of data-rich benchmarks.

Audio-Visual Synchronization

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

Improving Isochronous Machine Translation with Target Factors and Auxiliary Counters

no code implementations22 May 2023 Proyag Pal, Brian Thompson, Yogesh Virkar, Prashant Mathur, Alexandra Chronopoulou, Marcello Federico

To translate speech for automatic dubbing, machine translation needs to be isochronous, i. e. translated speech needs to be aligned with the source in terms of speech durations.

Decoder Machine Translation +1

Improving Robustness of Retrieval Augmented Translation via Shuffling of Suggestions

no code implementations11 Oct 2022 Cuong Hoang, Devendra Sachan, Prashant Mathur, Brian Thompson, Marcello Federico

Several recent studies have reported dramatic performance improvements in neural machine translation (NMT) by augmenting translation at inference time with fuzzy-matches retrieved from a translation memory (TM).

Machine Translation NMT +2

Improving Retrieval Augmented Neural Machine Translation by Controlling Source and Fuzzy-Match Interactions

no code implementations10 Oct 2022 Cuong Hoang, Devendra Sachan, Prashant Mathur, Brian Thompson, Marcello Federico

We explore zero-shot adaptation, where a general-domain model has access to customer or domain specific parallel data at inference time, but not during training.

Machine Translation Retrieval +2

Automatic Evaluation and Analysis of Idioms in Neural Machine Translation

1 code implementation10 Oct 2022 Christos Baziotis, Prashant Mathur, Eva Hasler

A major open problem in neural machine translation (NMT) is the translation of idiomatic expressions, such as "under the weather".

Machine Translation NMT +1

Embarrassingly Easy Document-Level MT Metrics: How to Convert Any Pretrained Metric Into a Document-Level Metric

1 code implementation27 Sep 2022 Giorgos Vernikos, Brian Thompson, Prashant Mathur, Marcello Federico

Our experimental results support our initial hypothesis and show that a simple extension of the metrics permits them to take advantage of context to resolve ambiguities in the reference.

Machine Translation Sentence

Isometric MT: Neural Machine Translation for Automatic Dubbing

no code implementations16 Dec 2021 Surafel M. Lakew, Yogesh Virkar, Prashant Mathur, Marcello Federico

Automatic dubbing (AD) is among the machine translation (MT) use cases where translations should match a given length to allow for synchronicity between source and target speech.

Machine Translation Re-Ranking +2

Isochrony-Aware Neural Machine Translation for Automatic Dubbing

no code implementations16 Dec 2021 Derek Tam, Surafel M. Lakew, Yogesh Virkar, Prashant Mathur, Marcello Federico

We introduce the task of isochrony-aware machine translation which aims at generating translations suitable for dubbing.

Machine Translation Sentence +1

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

Generating titles for millions of browse pages on an e-Commerce site

no code implementations WS 2017 Prashant Mathur, Nicola Ueffing, Gregor Leusch

We present two approaches to generate titles for browse pages in five different languages, namely English, German, French, Italian and Spanish.

Automatic Post-Editing Question Answering +2

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