Search Results for author: Santanu Pal

Found 42 papers, 0 papers with code

Findings of the 2021 Conference on Machine Translation (WMT21)

no code implementations WMT (EMNLP) 2021 Farhad Akhbardeh, Arkady Arkhangorodsky, Magdalena Biesialska, Ondřej Bojar, Rajen Chatterjee, Vishrav Chaudhary, Marta R. Costa-Jussa, Cristina España-Bonet, Angela Fan, Christian Federmann, Markus Freitag, Yvette Graham, Roman Grundkiewicz, Barry Haddow, Leonie Harter, Kenneth Heafield, Christopher Homan, Matthias Huck, Kwabena Amponsah-Kaakyire, Jungo Kasai, Daniel Khashabi, Kevin Knight, Tom Kocmi, Philipp Koehn, Nicholas Lourie, Christof Monz, Makoto Morishita, Masaaki Nagata, Ajay Nagesh, Toshiaki Nakazawa, Matteo Negri, Santanu Pal, Allahsera Auguste Tapo, Marco Turchi, Valentin Vydrin, Marcos Zampieri

This paper presents the results of the newstranslation task, the multilingual low-resourcetranslation for Indo-European languages, thetriangular translation task, and the automaticpost-editing task organised as part of the Con-ference on Machine Translation (WMT) 2021. In the news task, participants were asked tobuild machine translation systems for any of10 language pairs, to be evaluated on test setsconsisting mainly of news stories.

Machine Translation Translation

WT: Wipro AI Submissions to the WAT 2020

no code implementations AACL (WAT) 2020 Santanu Pal

In this paper we present an English–Hindi and Hindi–English neural machine translation (NMT) system, submitted to the Translation shared Task organized at WAT 2020.

Machine Translation Translation

Is Attention always needed? A Case Study on Language Identification from Speech

no code implementations5 Oct 2021 Atanu Mandal, Santanu Pal, Indranil Dutta, Mahidas Bhattacharya, Sudip Kumar Naskar

LID, therefore, plays a very important role in situations where ASR based systems cannot parse the uttered language in multilingual contexts causing failure in speech recognition.

Automatic Speech Recognition General Classification +2

MMPE: A Multi-Modal Interface using Handwriting, Touch Reordering, and Speech Commands for Post-Editing Machine Translation

no code implementations ACL 2020 Nico Herbig, Santanu Pal, Tim D{\"u}wel, Kalliopi Meladaki, Mahsa Monshizadeh, Vladislav Hnatovskiy, Antonio Kr{\"u}ger, Josef van Genabith

The shift from traditional translation to post-editing (PE) of machine-translated (MT) text can save time and reduce errors, but it also affects the design of translation interfaces, as the task changes from mainly generating text to correcting errors within otherwise helpful translation proposals.

Machine Translation Translation

MMPE: A Multi-Modal Interface for Post-Editing Machine Translation

no code implementations ACL 2020 Nico Herbig, Tim D{\"u}wel, Santanu Pal, Kalliopi Meladaki, Mahsa Monshizadeh, Antonio Kr{\"u}ger, Josef van Genabith

On the other hand, speech and multi-modal combinations of select {\&} speech are considered suitable for replacements and insertions but offer less potential for deletion and reordering.

Machine Translation Translation

UDS--DFKI Submission to the WMT2019 Similar Language Translation Shared Task

no code implementations16 Aug 2019 Santanu Pal, Marcos Zampieri, Josef van Genabith

The first edition of this shared task featured data from three pairs of similar languages: Czech and Polish, Hindi and Nepali, and Portuguese and Spanish.

Translation

The Transference Architecture for Automatic Post-Editing

no code implementations COLING 2020 Santanu Pal, Hongfei Xu, Nico Herbig, Sudip Kumar Naskar, Antonio Krueger, Josef van Genabith

In automatic post-editing (APE) it makes sense to condition post-editing (pe) decisions on both the source (src) and the machine translated text (mt) as input.

Automatic Post-Editing

Improving CAT Tools in the Translation Workflow: New Approaches and Evaluation

no code implementations WS 2019 Mihaela Vela, Santanu Pal, Marcos Zampieri, Sudip Kumar Naskar, Josef van Genabith

User feedback revealed that the users preferred using CATaLog Online over existing CAT tools in some respects, especially by selecting the output of the MT system and taking advantage of the color scheme for TM suggestions.

Automatic Post-Editing Translation

USAAR-DFKI -- The Transference Architecture for English--German Automatic Post-Editing

no code implementations WS 2019 Santanu Pal, Hongfei Xu, Nico Herbig, Antonio Kr{\"u}ger, Josef van Genabith

In this paper we present an English{--}German Automatic Post-Editing (APE) system called transference, submitted to the APE Task organized at WMT 2019.

Automatic Post-Editing Translation

UDS--DFKI Submission to the WMT2019 Czech--Polish Similar Language Translation Shared Task

no code implementations WS 2019 Santanu Pal, Marcos Zampieri, Josef van Genabith

The first edition of this shared task featured data from three pairs of similar languages: Czech and Polish, Hindi and Nepali, and Portuguese and Spanish.

Translation

JU-Saarland Submission to the WMT2019 English--Gujarati Translation Shared Task

no code implementations WS 2019 Riktim Mondal, Shankha Raj Nayek, Aditya Chowdhury, Santanu Pal, Sudip Kumar Naskar, Josef van Genabith

In this paper we describe our joint submission (JU-Saarland) from Jadavpur University and Saarland University in the WMT 2019 news translation shared task for English{--}Gujarati language pair within the translation task sub-track.

Machine Translation Translation

Integrating Artificial and Human Intelligence for Efficient Translation

no code implementations7 Mar 2019 Nico Herbig, Santanu Pal, Josef van Genabith, Antonio Krüger

Current advances in machine translation increase the need for translators to switch from traditional translation to post-editing of machine-translated text, a process that saves time and improves quality.

Machine Translation Translation

Keep It or Not: Word Level Quality Estimation for Post-Editing

no code implementations WS 2018 Prasenjit Basu, Santanu Pal, Sudip Kumar Naskar

The paper presents our participation in the WMT 2018 shared task on word level quality estimation (QE) of machine translated (MT) text, i. e., to predict whether a word in MT output for a given source context is correctly translated and hence should be retained in the post-edited translation (PE), or not.

Language Modelling Machine Translation +1

A Transformer-Based Multi-Source Automatic Post-Editing System

no code implementations WS 2018 Santanu Pal, Nico Herbig, Antonio Kr{\"u}ger, Josef van Genabith

The proposed model is an extension of the transformer architecture: two separate self-attention-based encoders encode the machine translation output (mt) and the source (src), followed by a joint encoder that attends over a combination of these two encoded sequences (encsrc and encmt) for generating the post-edited sentence.

Automatic Post-Editing Translation

Discriminating between Indo-Aryan Languages Using SVM Ensembles

no code implementations COLING 2018 Alina Maria Ciobanu, Marcos Zampieri, Shervin Malmasi, Santanu Pal, Liviu P. Dinu

In this paper we present a system based on SVM ensembles trained on characters and words to discriminate between five similar languages of the Indo-Aryan family: Hindi, Braj Bhasha, Awadhi, Bhojpuri, and Magahi.

Language Identification

A Neural Approach to Language Variety Translation

no code implementations COLING 2018 Marta R. Costa-jussà, Marcos Zampieri, Santanu Pal

In this paper we present the first neural-based machine translation system trained to translate between standard national varieties of the same language.

Machine Translation Translation

A Deep Learning Based Approach to Transliteration

no code implementations WS 2018 Soumyadeep Kundu, Sayantan Paul, Santanu Pal

In this paper, we propose different architectures for language independent machine transliteration which is extremely important for natural language processing (NLP) applications.

Information Retrieval Translation +1

Neural Automatic Post-Editing Using Prior Alignment and Reranking

no code implementations EACL 2017 Santanu Pal, Sudip Kumar Naskar, Mihaela Vela, Qun Liu, Josef van Genabith

APE translations produced by our system show statistically significant improvements over the first-stage MT, phrase-based APE and the best reported score on the WMT 2016 APE dataset by a previous neural APE system.

Automatic Post-Editing Re-Ranking +1

Multi-Engine and Multi-Alignment Based Automatic Post-Editing and its Impact on Translation Productivity

no code implementations COLING 2016 Santanu Pal, Sudip Kumar Naskar, Josef van Genabith

In the paper we show that parallel system combination in the APE stage of a sequential MT-APE combination yields substantial translation improvements both measured in terms of automatic evaluation metrics as well as in terms of productivity improvements measured in a post-editing experiment.

Automatic Post-Editing Translation

CATaLog Online: Porting a Post-editing Tool to the Web

no code implementations LREC 2016 Santanu Pal, Marcos Zampieri, Sudip Kumar Naskar, Tapas Nayak, Mihaela Vela, Josef van Genabith

The tool features a number of editing and log functions similar to the desktop version of CATaLog enhanced with several new features that we describe in detail in this paper.

Machine Translation Translation

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