Santanu Pal


2021

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Findings of the 2021 Conference on Machine Translation (WMT21)
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
Proceedings of the Sixth Conference on Machine Translation

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. The taskwas also opened up to additional test suites toprobe specific aspects of translation.

2020

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The Transference Architecture for Automatic Post-Editing
Santanu Pal | Hongfei Xu | Nico Herbig | Sudip Kumar Naskar | Antonio Krüger | Josef van Genabith
Proceedings of the 28th International Conference on Computational Linguistics

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. This has led to multi-encoder based neural APE approaches. A research challenge now is the search for architectures that best support the capture, preparation and provision of src and mt information and its integration with pe decisions. In this paper we present an efficient multi-encoder based APE model, called transference. Unlike previous approaches, it (i) uses a transformer encoder block for src, (ii) followed by a decoder block, but without masking for self-attention on mt, which effectively acts as second encoder combining src –> mt, and (iii) feeds this representation into a final decoder block generating pe. Our model outperforms the best performing systems by 1 BLEU point on the WMT 2016, 2017, and 2018 English–German APE shared tasks (PBSMT and NMT). Furthermore, the results of our model on the WMT 2019 APE task using NMT data shows a comparable performance to the state-of-the-art system. The inference time of our model is similar to the vanilla transformer-based NMT system although our model deals with two separate encoders. We further investigate the importance of our newly introduced second encoder and find that a too small amount of layers does hurt the performance, while reducing the number of layers of the decoder does not matter much.

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MMPE: A Multi-Modal Interface for Post-Editing Machine Translation
Nico Herbig | Tim Düwel | Santanu Pal | Kalliopi Meladaki | Mahsa Monshizadeh | Antonio Krüger | Josef van Genabith
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Current advances in machine translation (MT) increase the need for translators to switch from traditional translation to post-editing (PE) of machine-translated text, a process that saves time and reduces errors. This affects the design of translation interfaces, as the task changes from mainly generating text to correcting errors within otherwise helpful translation proposals. Since this paradigm shift offers potential for modalities other than mouse and keyboard, we present MMPE, the first prototype to combine traditional input modes with pen, touch, and speech modalities for PE of MT. The results of an evaluation with professional translators suggest that pen and touch interaction are suitable for deletion and reordering tasks, while they are of limited use for longer insertions. 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. Overall, participants were enthusiastic about the new modalities and saw them as good extensions to mouse & keyboard, but not as a complete substitute.

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MMPE: A Multi-Modal Interface using Handwriting, Touch Reordering, and Speech Commands for Post-Editing Machine Translation
Nico Herbig | Santanu Pal | Tim Düwel | Kalliopi Meladaki | Mahsa Monshizadeh | Vladislav Hnatovskiy | Antonio Krüger | Josef van Genabith
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations

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. Since this paradigm shift offers potential for modalities other than mouse and keyboard, we present MMPE, the first prototype to combine traditional input modes with pen, touch, and speech modalities for PE of MT. Users can directly cross out or hand-write new text, drag and drop words for reordering, or use spoken commands to update the text in place. All text manipulations are logged in an easily interpretable format to simplify subsequent translation process research. The results of an evaluation with professional translators suggest that pen and touch interaction are suitable for deletion and reordering tasks, while speech and multi-modal combinations of select & speech are considered suitable for replacements and insertions. Overall, experiment participants were enthusiastic about the new modalities and saw them as useful extensions to mouse & keyboard, but not as a complete substitute.

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WT: Wipro AI Submissions to the WAT 2020
Santanu Pal
Proceedings of the 7th Workshop on Asian Translation

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. We trained a multilingual NMT system based on transformer architecture. In this paper we show: (i) how effective pre-processing helps to improve performance, (ii) how synthetic data through back-translation from available monolingual data can help in overall translation performance, (iii) how language similarity can aid more onto it. Our submissions ranked 1st in both English to Hindi and Hindi to English translation achieving BLEU 20.80 and 29.59 respectively.

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Findings of the 2020 Conference on Machine Translation (WMT20)
Loïc Barrault | Magdalena Biesialska | Ondřej Bojar | Marta R. Costa-jussà | Christian Federmann | Yvette Graham | Roman Grundkiewicz | Barry Haddow | Matthias Huck | Eric Joanis | Tom Kocmi | Philipp Koehn | Chi-kiu Lo | Nikola Ljubešić | Christof Monz | Makoto Morishita | Masaaki Nagata | Toshiaki Nakazawa | Santanu Pal | Matt Post | Marcos Zampieri
Proceedings of the Fifth Conference on Machine Translation

This paper presents the results of the news translation task and the similar language translation task, both organised alongside the Conference on Machine Translation (WMT) 2020. In the news task, participants were asked to build machine translation systems for any of 11 language pairs, to be evaluated on test sets consisting mainly of news stories. The task was also opened up to additional test suites to probe specific aspects of translation. In the similar language translation task, participants built machine translation systems for translating between closely related pairs of languages.

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Neural Machine Translation for Similar Languages: The Case of Indo-Aryan Languages
Santanu Pal | Marcos Zampieri
Proceedings of the Fifth Conference on Machine Translation

In this paper we present the WIPRO-RIT systems submitted to the Similar Language Translation shared task at WMT 2020. The second edition of this shared task featured parallel data from pairs/groups of similar languages from three different language families: Indo-Aryan languages (Hindi and Marathi), Romance languages (Catalan, Portuguese, and Spanish), and South Slavic Languages (Croatian, Serbian, and Slovene). We report the results obtained by our systems in translating from Hindi to Marathi and from Marathi to Hindi. WIPRO-RIT achieved competitive performance ranking 1st in Marathi to Hindi and 2nd in Hindi to Marathi translation among 22 systems.

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Improving the Multi-Modal Post-Editing (MMPE) CAT Environment based on Professional Translators’ Feedback
Nico Herbig | Santanu Pal | Tim Düwel | Raksha Shenoy | Antonio Krüger | Josef van Genabith
Proceedings of 1st Workshop on Post-Editing in Modern-Day Translation

2019

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Findings of the 2019 Conference on Machine Translation (WMT19)
Loïc Barrault | Ondřej Bojar | Marta R. Costa-jussà | Christian Federmann | Mark Fishel | Yvette Graham | Barry Haddow | Matthias Huck | Philipp Koehn | Shervin Malmasi | Christof Monz | Mathias Müller | Santanu Pal | Matt Post | Marcos Zampieri
Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)

This paper presents the results of the premier shared task organized alongside the Conference on Machine Translation (WMT) 2019. Participants were asked to build machine translation systems for any of 18 language pairs, to be evaluated on a test set of news stories. The main metric for this task is human judgment of translation quality. The task was also opened up to additional test suites to probe specific aspects of translation.

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JU-Saarland Submission to the WMT2019 English–Gujarati Translation Shared Task
Riktim Mondal | Shankha Raj Nayek | Aditya Chowdhury | Santanu Pal | Sudip Kumar Naskar | Josef van Genabith
Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)

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. Our baseline and primary submissions are built using Recurrent neural network (RNN) based neural machine translation (NMT) system which follows attention mechanism. Given the fact that the two languages belong to different language families and there is not enough parallel data for this language pair, building a high quality NMT system for this language pair is a difficult task. We produced synthetic data through back-translation from available monolingual data. We report the translation quality of our English–Gujarati and Gujarati–English NMT systems trained at word, byte-pair and character encoding levels where RNN at word level is considered as the baseline and used for comparison purpose. Our English–Gujarati system ranked in the second position in the shared task.

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USAAR-DFKI – The Transference Architecture for English–German Automatic Post-Editing
Santanu Pal | Hongfei Xu | Nico Herbig | Antonio Krüger | Josef van Genabith
Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2)

In this paper we present an English–German Automatic Post-Editing (APE) system called transference, submitted to the APE Task organized at WMT 2019. Our transference model is based on a multi-encoder transformer architecture. Unlike previous approaches, it (i) uses a transformer encoder block for src, (ii) followed by a transformer decoder block, but without masking, for self-attention on mt, which effectively acts as second encoder combining src –> mt, and (iii) feeds this representation into a final decoder block generating pe. Our model improves over the raw black-box neural machine translation system by 0.9 and 1.0 absolute BLEU points on the WMT 2019 APE development and test set. Our submission ranked 3rd, however compared to the two top systems, performance differences are not statistically significant.

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UDSDFKI Submission to the WMT2019 Czech–Polish Similar Language Translation Shared Task
Santanu Pal | Marcos Zampieri | Josef van Genabith
Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2)

In this paper we present the UDS-DFKI system submitted to the Similar Language Translation shared task at WMT 2019. 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. Participants could choose to participate in any of these three tracks and submit system outputs in any translation direction. We report the results obtained by our system in translating from Czech to Polish and comment on the impact of out-of-domain test data in the performance of our system. UDS-DFKI achieved competitive performance ranking second among ten teams in Czech to Polish translation.

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Improving CAT Tools in the Translation Workflow: New Approaches and Evaluation
Mihaela Vela | Santanu Pal | Marcos Zampieri | Sudip Naskar | Josef van Genabith
Proceedings of Machine Translation Summit XVII: Translator, Project and User Tracks

2018

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A Deep Learning Based Approach to Transliteration
Soumyadeep Kundu | Sayantan Paul | Santanu Pal
Proceedings of the Seventh Named Entities Workshop

In this paper, we propose different architectures for language independent machine transliteration which is extremely important for natural language processing (NLP) applications. Though a number of statistical models for transliteration have already been proposed in the past few decades, we proposed some neural network based deep learning architectures for the transliteration of named entities. Our transliteration systems adapt two different neural machine translation (NMT) frameworks: recurrent neural network and convolutional sequence to sequence based NMT. It is shown that our method provides quite satisfactory results when it comes to multi lingual machine transliteration. Our submitted runs are an ensemble of different transliteration systems for all the language pairs. In the NEWS 2018 Shared Task on Transliteration, our method achieves top performance for the En–Pe and Pe–En language pairs and comparable results for other cases.

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Discriminating between Indo-Aryan Languages Using SVM Ensembles
Alina Maria Ciobanu | Marcos Zampieri | Shervin Malmasi | Santanu Pal | Liviu P. Dinu
Proceedings of the Fifth Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial 2018)

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. The system competed in the Indo-Aryan Language Identification (ILI) shared task organized within the VarDial Evaluation Campaign 2018. Our best entry in the competition, named ILIdentification, scored 88.95% F1 score and it was ranked 3rd out of 8 teams.

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A Neural Approach to Language Variety Translation
Marta R. Costa-jussà | Marcos Zampieri | Santanu Pal
Proceedings of the Fifth Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial 2018)

In this paper we present the first neural-based machine translation system trained to translate between standard national varieties of the same language. We take the pair Brazilian - European Portuguese as an example and compare the performance of this method to a phrase-based statistical machine translation system. We report a performance improvement of 0.9 BLEU points in translating from European to Brazilian Portuguese and 0.2 BLEU points when translating in the opposite direction. We also carried out a human evaluation experiment with native speakers of Brazilian Portuguese which indicates that humans prefer the output produced by the neural-based system in comparison to the statistical system.

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Keep It or Not: Word Level Quality Estimation for Post-Editing
Prasenjit Basu | Santanu Pal | Sudip Kumar Naskar
Proceedings of the Third Conference on Machine Translation: Shared Task Papers

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. To perform the QE task, we measure the similarity of the source context of the target MT word with the context for which the word is retained in PE in the training data. This is achieved in two different ways, using Bag-of-Words (BoW) model and Document-to-Vector (Doc2Vec) model. In the BoW model, we compute the cosine similarity while in the Doc2Vec model we consider the Doc2Vec similarity. By applying the Kneedle algorithm on the F1mult vs. similarity score plot, we derive the threshold based on which OK/BAD decisions are taken for the MT words. Experimental results revealed that the Doc2Vec model performs better than the BoW model on the word level QE task.

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A Transformer-Based Multi-Source Automatic Post-Editing System
Santanu Pal | Nico Herbig | Antonio Krüger | Josef van Genabith
Proceedings of the Third Conference on Machine Translation: Shared Task Papers

This paper presents our English–German Automatic Post-Editing (APE) system submitted to the APE Task organized at WMT 2018 (Chatterjee et al., 2018). 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. We compare this multi-source architecture (i.e, {src, mt} → pe) to a monolingual transformer (i.e., mt → pe) model and an ensemble combining the multi-source {src, mt} → pe and single-source mt → pe models. For both the PBSMT and the NMT task, the ensemble yields the best results, followed by the multi-source model and last the single-source approach. Our best model, the ensemble, achieves a BLEU score of 66.16 and 74.22 for the PBSMT and NMT task, respectively.

2017

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Neural Automatic Post-Editing Using Prior Alignment and Reranking
Santanu Pal | Sudip Kumar Naskar | Mihaela Vela | Qun Liu | Josef van Genabith
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers

We present a second-stage machine translation (MT) system based on a neural machine translation (NMT) approach to automatic post-editing (APE) that improves the translation quality provided by a first-stage MT system. Our APE system (APE_Sym) is an extended version of an attention based NMT model with bilingual symmetry employing bidirectional models, mt–pe and pe–mt. 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. Re-ranking (APE_Rerank) of the n-best translations from the phrase-based APE and APE_Sym systems provides further substantial improvements over the symmetric neural APE model. Human evaluation confirms that the APE_Rerank generated PE translations improve on the previous best neural APE system at WMT 2016.

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Multi-source Neural Automatic Post-Editing: FBK’s participation in the WMT 2017 APE shared task
Rajen Chatterjee | M. Amin Farajian | Matteo Negri | Marco Turchi | Ankit Srivastava | Santanu Pal
Proceedings of the Second Conference on Machine Translation

2016

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JU-USAAR: A Domain Adaptive MT System
Koushik Pahari | Alapan Kuila | Santanu Pal | Sudip Kumar Naskar | Sivaji Bandyopadhyay | Josef van Genabith
Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers

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WMT2016: A Hybrid Approach to Bilingual Document Alignment
Sainik Mahata | Dipankar Das | Santanu Pal
Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers

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USAAR: An Operation Sequential Model for Automatic Statistical Post-Editing
Santanu Pal | Marcos Zampieri | Josef van Genabith
Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers

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A Neural Network based Approach to Automatic Post-Editing
Santanu Pal | Sudip Kumar Naskar | Mihaela Vela | Josef van Genabith
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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Multi-Engine and Multi-Alignment Based Automatic Post-Editing and its Impact on Translation Productivity
Santanu Pal | Sudip Kumar Naskar | Josef van Genabith
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

In this paper we combine two strands of machine translation (MT) research: automatic post-editing (APE) and multi-engine (system combination) MT. APE systems learn a target-language-side second stage MT system from the data produced by human corrected output of a first stage MT system, to improve the output of the first stage MT in what is essentially a sequential MT system combination architecture. At the same time, there is a rich research literature on parallel MT system combination where the same input is fed to multiple engines and the best output is selected or smaller sections of the outputs are combined to obtain improved translation output. 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. We also show that system combination on the level of APE alignments yields further improvements. Overall our APE system yields statistically significant improvement of 5.9% relative BLEU over a strong baseline (English–Italian Google MT) and 21.76% productivity increase in a human post-editing experiment with professional translators.

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CATaLog Online: A Web-based CAT Tool for Distributed Translation with Data Capture for APE and Translation Process Research
Santanu Pal | Sudip Kumar Naskar | Marcos Zampieri | Tapas Nayak | Josef van Genabith
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: System Demonstrations

We present a free web-based CAT tool called CATaLog Online which provides a novel and user-friendly online CAT environment for post-editors/translators. The goal is to support distributed translation, reduce post-editing time and effort, improve the post-editing experience and capture data for incremental MT/APE (automatic post-editing) and translation process research. The tool supports individual as well as batch mode file translation and provides translations from three engines – translation memory (TM), MT and APE. TM suggestions are color coded to accelerate the post-editing task. The users can integrate their personal TM/MT outputs. The tool remotely monitors and records post-editing activities generating an extensive range of post-editing logs.

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CATaLog Online: Porting a Post-editing Tool to the Web
Santanu Pal | Marcos Zampieri | Sudip Kumar Naskar | Tapas Nayak | Mihaela Vela | Josef van Genabith
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

This paper presents CATaLog online, a new web-based MT and TM post-editing tool. CATaLog online is a freeware software that can be used through a web browser and it requires only a simple registration. 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. CATaLog online is designed to allow users to post-edit both translation memory segments as well as machine translation output. The tool provides a complete set of log information currently not available in most commercial CAT tools. Log information can be used both for project management purposes as well as for the study of the translation process and translator’s productivity.

2015

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UdS-Sant: English–German Hybrid Machine Translation System
Santanu Pal | Sudip Naskar | Josef van Genabith
Proceedings of the Tenth Workshop on Statistical Machine Translation

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USAAR-SAPE: An English–Spanish Statistical Automatic Post-Editing System
Santanu Pal | Mihaela Vela | Sudip Kumar Naskar | Josef van Genabith
Proceedings of the Tenth Workshop on Statistical Machine Translation

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CATaLog: New Approaches to TM and Post Editing Interfaces
Tapas Nayek | Sudip Kumar Naskar | Santanu Pal | Marcos Zampieri | Mihaela Vela | Josef van Genabith
Proceedings of the Workshop Natural Language Processing for Translation Memories

2014

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Word Alignment-Based Reordering of Source Chunks in PB-SMT
Santanu Pal | Sudip Kumar Naskar | Sivaji Bandyopadhyay
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

Reordering poses a big challenge in statistical machine translation between distant language pairs. The paper presents how reordering between distant language pairs can be handled efficiently in phrase-based statistical machine translation. The problem of reordering between distant languages has been approached with prior reordering of the source text at chunk level to simulate the target language ordering. Prior reordering of the source chunks is performed in the present work by following the target word order suggested by word alignment. The testset is reordered using monolingual MT trained on source and reordered source. This approach of prior reordering of the source chunks was compared with pre-ordering of source words based on word alignments and the traditional approach of prior source reordering based on language-pair specific reordering rules. The effects of these reordering approaches were studied on an English--Bengali translation task, a language pair with different word order. From the experimental results it was found that word alignment based reordering of the source chunks is more effective than the other reordering approaches, and it produces statistically significant improvements over the baseline system on BLEU. On manual inspection we found significant improvements in terms of word alignments.

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Automatic Building and Using Parallel Resources for SMT from Comparable Corpora
Santanu Pal | Partha Pakray | Sudip Kumar Naskar
Proceedings of the 3rd Workshop on Hybrid Approaches to Machine Translation (HyTra)

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Manawi: Using Multi-Word Expressions and Named Entities to Improve Machine Translation
Liling Tan | Santanu Pal
Proceedings of the Ninth Workshop on Statistical Machine Translation

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How Sentiment Analysis Can Help Machine Translation
Santanu Pal | Braja Gopal Patra | Dipankar Das | Sudip Kumar Naskar | Sivaji Bandyopadhyay | Josef van Genabith
Proceedings of the 11th International Conference on Natural Language Processing

2013

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MWE Alignment in Phrase Based Statistical Machine Translation
Santanu Pal | Sudip Kumar Naskar | Sivaji Bandyopadhyay
Proceedings of Machine Translation Summit XIV: Papers

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Improving MT System Using Extracted Parallel Fragments of Text from Comparable Corpora
Rajdeep Gupta | Santanu Pal | Sivaji Bandyopadhyay
Proceedings of the Sixth Workshop on Building and Using Comparable Corpora

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A Hybrid Word Alignment Model for Phrase-Based Statistical Machine Translation
Santanu Pal | Sudip Naskar | Sivaji Bandyopadhyay
Proceedings of the Second Workshop on Hybrid Approaches to Translation

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Event and Event Actor Alignment in Phrase Based Statistical Machine Translation
Anup Kolya | Santanu Pal | Asif Ekbal | Sivaji Bandyopadhyay
Proceedings of the 11th Workshop on Asian Language Resources

2012

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Bootstrapping Method for Chunk Alignment in Phrase Based SMT
Santanu Pal | Sivaji Bandyopadhyay
Proceedings of the Joint Workshop on Exploiting Synergies between Information Retrieval and Machine Translation (ESIRMT) and Hybrid Approaches to Machine Translation (HyTra)

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Detection and Correction of Preposition and Determiner Errors in English: HOO 2012
Pinaki Bhaskar | Aniruddha Ghosh | Santanu Pal | Sivaji Bandyopadhyay
Proceedings of the Seventh Workshop on Building Educational Applications Using NLP

2011

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Handling Multiword Expressions in Phrase-Based Statistical Machine Translation
Santanu Pal | Tanmoy Chakraborty | Sivaji Bandyopadhyay
Proceedings of Machine Translation Summit XIII: Papers

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Shared Task System Description: Measuring the Compositionality of Bigrams using Statistical Methodologies
Tanmoy Chakraborty | Santanu Pal | Tapabrata Mondal | Tanik Saikh | Sivaju Bandyopadhyay
Proceedings of the Workshop on Distributional Semantics and Compositionality

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May I check the English of your paper!!!
Pinaki Bhaskar | Aniruddha Ghosh | Santanu Pal | Sivaji Bandyopadhyay
Proceedings of the 13th European Workshop on Natural Language Generation

2010

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JU: A Supervised Approach to Identify Semantic Relations from Paired Nominals
Santanu Pal | Partha Pakray | Dipankar Das | Sivaji Bandyopadhyay
Proceedings of the 5th International Workshop on Semantic Evaluation

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Automatic Extraction of Complex Predicates in Bengali
Dipankar Das | Santanu Pal | Tapabrata Mondal | Tanmoy Chakraborty | Sivaji Bandyopadhyay
Proceedings of the 2010 Workshop on Multiword Expressions: from Theory to Applications

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Handling Named Entities and Compound Verbs in Phrase-Based Statistical Machine Translation
Santanu Pal | Sudip Kumar Naskar | Pavel Pecina | Sivaji Bandyopadhyay | Andy Way
Proceedings of the 2010 Workshop on Multiword Expressions: from Theory to Applications