Sudip Kumar Naskar

Also published as: Sudip Kumar Naskar, Sudip Naskar


2021

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Fine-tuning BERT to classify COVID19 tweets containing symptoms
Rajarshi Roychoudhury | Sudip Naskar
Proceedings of the Sixth Social Media Mining for Health (#SMM4H) Workshop and Shared Task

Twitter is a valuable source of patient-generated data that has been used in various population health studies. The first step in many of these studies is to identify and capture Twitter messages (tweets) containing medication mentions. Identifying personal mentions of COVID19 symptoms requires distinguishing personal mentions from other mentions such as symptoms reported by others and references to news articles or other sources. In this article, we describe our submission to Task 6 of the Social Media Mining for Health Applications (SMM4H) Shared Task 2021. This task challenged participants to classify tweets where the target classes are:(1) self-reports,(2) non-personal reports, and (3) literature/news mentions. Our system used a handcrafted preprocessing and word embeddings from BERT encoder model. We achieved an F1 score of 93%

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JUNLP@DravidianLangTech-EACL2021: Offensive Language Identification in Dravidian Langauges
Avishek Garain | Atanu Mandal | Sudip Kumar Naskar
Proceedings of the First Workshop on Speech and Language Technologies for Dravidian Languages

Offensive language identification has been an active area of research in natural language processing. With the emergence of multiple social media platforms offensive language identification has emerged as a need of the hour. Traditional offensive language identification models fail to deliver acceptable results as social media contents are largely in multilingual and are code-mixed in nature. This paper tries to resolve this problem by using IndicBERT and BERT architectures, to facilitate identification of offensive languages for Kannada-English, Malayalam-English, and Tamil-English code-mixed language pairs extracted from social media. The presented approach when evaluated on the test corpus provided precision, recall, and F1 score for language pair Kannada-English as 0.62, 0.71, and 0.66, respectively, for language pair Malayalam-English as 0.77, 0.43, and 0.53, respectively, and for Tamil-English as 0.71, 0.74, and 0.72, respectively.

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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A New Approach to Claim Check-Worthiness Prediction and Claim Verification
Shukrity Si | Anisha Datta | Sudip Naskar
Proceedings of the 17th International Conference on Natural Language Processing (ICON)

The more we are advancing towards a modern world, the more it opens the path to falsification in every aspect of life. Even in case of knowing the surrounding, common people can not judge the actual scenario as the promises, comments and opinions of the influential people at power keep changing every day. Therefore computationally determining the truthfulness of such claims and comments has a very important societal impact. This paper describes a unique method to extract check-worthy claims from the 2016 US presidential debates and verify the truthfulness of the check-worthy claims. We classify the claims for check-worthiness with our modified Tf-Idf model which is used in background training on fact-checking news articles (NBC News and Washington Post). We check the truthfulness of the claims by using POS, sentiment score and cosine similarity features.

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A Rule Based Lightweight Bengali Stemmer
Souvick Das | Rajat Pandit | Sudip Kumar Naskar
Proceedings of the 17th International Conference on Natural Language Processing (ICON)

In the field of Natural Language Processing (NLP) the process of stemming plays a significant role. Stemmer transforms an inflected word to its root form. Stemmer significantly increases the efficiency of Information Retrieval (IR) systems. It is a very basic yet fundamental text pre-processing task widely used in many NLP tasks. Several important works on stemming have been carried out by researchers in English and other major languages. In this paper, we study and review existing works on stemming in Bengali and other Indian languages. Finally, we propose a rule based approach that explores Bengali morphology and leverages WordNet to achieve better accuracy. Our algorithm produced stemming accuracy of 98.86% for Nouns and 99.75% for Verbs.

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Deep Neural Model for Manipuri Multiword Named Entity Recognition with Unsupervised Cluster Feature
Jimmy Laishram | Kishorjit Nongmeikapam | Sudip Naskar
Proceedings of the 17th International Conference on Natural Language Processing (ICON)

The recognition task of Multi-Word Named Entities (MNEs) in itself is a challenging task when the language is inflectional and agglutinative. Having breakthrough NLP researches with deep neural network and language modelling techniques, the applicability of such techniques/algorithms for Indian language like Manipuri remains unanswered. In this paper an attempt to recognize Manipuri MNE is performed using a Long Short Term Memory (LSTM) recurrent neural network model in conjunction with Part Of Speech (POS) embeddings. To further improve the classification accuracy, word cluster information using K-means clustering approach is added as a feature embedding. The cluster information is generated using a Skip-gram based words vector that contains the semantic and syntactic information of each word. The model so proposed does not use extensive language morphological features to elevate its accuracy. Finally the model’s performance is compared with the other machine learning based Manipuri MNE models.

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Proceedings of the 17th International Conference on Natural Language Processing (ICON): TechDOfication 2020 Shared Task
Dipti Misra Sharma | Asif Ekbal | Karunesh Arora | Sudip Kumar Naskar | Dipankar Ganguly | Sobha L | Radhika Mamidi | Sunita Arora | Pruthwik Mishra | Vandan Mujadia
Proceedings of the 17th International Conference on Natural Language Processing (ICON): TechDOfication 2020 Shared Task

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Proceedings of the 17th International Conference on Natural Language Processing (ICON): TermTraction 2020 Shared Task
Dipti Misra Sharma | Asif Ekbal | Karunesh Arora | Sudip Kumar Naskar | Dipankar Ganguly | Sobha L | Radhika Mamidi | Sunita Arora | Pruthwik Mishra | Vandan Mujadia
Proceedings of the 17th International Conference on Natural Language Processing (ICON): TermTraction 2020 Shared Task

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Proceedings of the 17th International Conference on Natural Language Processing (ICON): Adap-MT 2020 Shared Task
Dipti Misra Sharma | Asif Ekbal | Karunesh Arora | Sudip Kumar Naskar | Dipankar Ganguly | Sobha L | Radhika Mamidi | Sunita Arora | Pruthwik Mishra | Vandan Mujadia
Proceedings of the 17th International Conference on Natural Language Processing (ICON): Adap-MT 2020 Shared Task

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Spyder: Aggression Detection on Multilingual Tweets
Anisha Datta | Shukrity Si | Urbi Chakraborty | Sudip Kumar Naskar
Proceedings of the Second Workshop on Trolling, Aggression and Cyberbullying

In the last few years, hate speech and aggressive comments have covered almost all the social media platforms like facebook, twitter etc. As a result hatred is increasing. This paper describes our (Team name: Spyder) participation in the Shared Task on Aggression Detection organised by TRAC-2, Second Workshop on Trolling, Aggression and Cyberbullying. The Organizers provided datasets in three languages – English, Hindi and Bengali. The task was to classify each instance of the test sets into three categories – “Overtly Aggressive” (OAG), “Covertly Aggressive” (CAG) and “Non-Aggressive” (NAG). In this paper, we propose three different models using Tf-Idf, sentiment polarity and machine learning based classifiers. We obtained f1 score of 43.10%, 59.45% and 44.84% respectively for English, Hindi and Bengali.

2019

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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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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

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JU_ETCE_17_21 at SemEval-2019 Task 6: Efficient Machine Learning and Neural Network Approaches for Identifying and Categorizing Offensive Language in Tweets
Preeti Mukherjee | Mainak Pal | Somnath Banerjee | Sudip Kumar Naskar
Proceedings of the 13th International Workshop on Semantic Evaluation

This paper describes our system submissions as part of our participation (team name: JU_ETCE_17_21) in the SemEval 2019 shared task 6: “OffensEval: Identifying and Catego- rizing Offensive Language in Social Media”. We participated in all the three sub-tasks: i) Sub-task A: offensive language identification, ii) Sub-task B: automatic categorization of of- fense types, and iii) Sub-task C: offense target identification. We employed machine learn- ing as well as deep learning approaches for the sub-tasks. We employed Convolutional Neural Network (CNN) and Recursive Neu- ral Network (RNN) Long Short-Term Memory (LSTM) with pre-trained word embeddings. We used both word2vec and Glove pre-trained word embeddings. We obtained the best F1- score using CNN based model for sub-task A, LSTM based model for sub-task B and Lo- gistic Regression based model for sub-task C. Our best submissions achieved 0.7844, 0.5459 and 0.48 F1-scores for sub-task A, sub-task B and sub-task C respectively.

2018

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ITER: Improving Translation Edit Rate through Optimizable Edit Costs
Joybrata Panja | Sudip Kumar Naskar
Proceedings of the Third Conference on Machine Translation: Shared Task Papers

The paper presents our participation in the WMT 2018 Metrics Shared Task. We propose an improved version of Translation Edit/Error Rate (TER). In addition to including the basic edit operations in TER, namely - insertion, deletion, substitution and shift, our metric also allows stem matching, optimizable edit costs and better normalization so as to correlate better with human judgement scores. The proposed metric shows much higher correlation with human judgments than TER.

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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.

2017

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Natural Language Programing with Automatic Code Generation towards Solving Addition-Subtraction Word Problems
Sourav Mandal | Sudip Kumar Naskar
Proceedings of the 14th International Conference on Natural Language Processing (ICON-2017)

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Unsupervised Morpheme Segmentation Through Numerical Weighting and Thresholding
Joy Mahapatra | Sudip Kumar Naskar
Proceedings of the 14th International Conference on Natural Language Processing (ICON-2017)

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Normalization of Social Media Text using Deep Neural Networks
Ajay Shankar Tiwari | Sudip Kumar Naskar
Proceedings of the 14th International Conference on Natural Language Processing (ICON-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.

2016

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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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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.

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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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Biomolecular Event Extraction using a Stacked Generalization based Classifier
Amit Majumder | Asif Ekbal | Sudip Kumar Naskar
Proceedings of the 13th International Conference on Natural Language Processing

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Statistical Natural Language Generation from Tabular Non-textual Data
Joy Mahapatra | Sudip Kumar Naskar | Sivaji Bandyopadhyay
Proceedings of the 9th International Natural Language Generation conference

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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.

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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Perception vs. reality: measuring machine translation post-editing productivity
Federico Gaspari | Antonio Toral | Sudip Kumar Naskar | Declan Groves | Andy Way
Proceedings of the 11th Conference of the Association for Machine Translation in the Americas

This paper presents a study of user-perceived vs real machine translation (MT) post-editing effort and productivity gains, focusing on two bidirectional language pairs: English—German and English—Dutch. Twenty experienced media professionals post-edited statistical MT output and also manually translated comparative texts within a production environment. The paper compares the actual post-editing time against the users’ perception of the effort and time required to post-edit the MT output to achieve publishable quality, thus measuring real (vs perceived) productivity gains. Although for all the language pairs users perceived MT post-editing to be slower, in fact it proved to be a faster option than manual translation for two translation directions out of four, i.e. for Dutch to English, and (marginally) for English to German. For further objective scrutiny, the paper also checks the correlation of three state-of-the-art automatic MT evaluation metrics (BLEU, METEOR and TER) with the actual post-editing time.

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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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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

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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.

2013

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A Web Application for the Diagnostic Evaluation of Machine Translation over Specific Linguistic Phenomena
Antonio Toral | Sudip Kumar Naskar | Joris Vreeke | Federico Gaspari | Declan Groves
Proceedings of the 2013 NAACL HLT Demonstration Session

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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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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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Meta-Evaluation of a Diagnostic Quality Metric for Machine Translation
Sudip Kumar Naskar | Antonio Toral | Federico Gaspari | Declan Groves
Proceedings of Machine Translation Summit XIV: Papers

2012

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A Diagnostic Evaluation Approach Targeting MT Systems for Indian Languages
Renu Balyan | Sudip Kumar Naskar | Antonio Toral | Niladri Chatterjee
Proceedings of the Workshop on Machine Translation and Parsing in Indian Languages

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Translation Quality-Based Supplementary Data Selection by Incremental Update of Translation Models
Pratyush Banerjee | Sudip Kumar Naskar | Johann Roturier | Andy Way | Josef van Genabith
Proceedings of COLING 2012

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Domain Adaptation in SMT of User-Generated Forum Content Guided by OOV Word Reduction: Normalization and/or Supplementary Data
Pratyush Banerjee | Sudip Kumar Naskar | Johann Roturier | Andy Way | Josef van Genabith
Proceedings of the 16th Annual conference of the European Association for Machine Translation

2011

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A Comparative Evaluation of Research vs. Online MT Systems
Antonio Toral | Federico Gaspari | Sudip Kumar Naskar | Andy Way
Proceedings of the 15th Annual conference of the European Association for Machine Translation

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Combining Semantic and Syntactic Generalization in Example-Based Machine Translation
Sarah Ebling | Andy Way | Martin Volk | Sudip Kumar Naskar
Proceedings of the 15th Annual conference of the European Association for Machine Translation

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Domain Adaptation in Statistical Machine Translation of User-Forum Data using Component Level Mixture Modelling
Pratyush Banerjee | Sudip Kumar Naskar | Johann Roturier | Andy Way | Josef van Genabith
Proceedings of Machine Translation Summit XIII: Papers

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A Framework for Diagnostic Evaluation of MT Based on Linguistic Checkpoints
Sudip Kumar Naskar | Antonio Toral | Federico Gaspari | Andy Way
Proceedings of Machine Translation Summit XIII: Papers

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The DCU machine translation systems for IWSLT 2011
Pratyush Banerjee | Hala Almaghout | Sudip Naskar | Johann Roturier | Jie Jiang | Andy Way | Josef van Genabith
Proceedings of the 8th International Workshop on Spoken Language Translation: Evaluation Campaign

In this paper, we provide a description of the Dublin City University’s (DCU) submissions in the IWSLT 2011 evaluationcampaign.1 WeparticipatedintheArabic-Englishand Chinese-English Machine Translation(MT) track translation tasks. We use phrase-based statistical machine translation (PBSMT) models to create the baseline system. Due to the open-domain nature of the data to be translated, we use domain adaptation techniques to improve the quality of translation. Furthermore, we explore target-side syntactic augmentation for an Hierarchical Phrase-Based (HPB) SMT model. Combinatory Categorial Grammar (CCG) is used to extract labels for target-side phrases and non-terminals in the HPB system. Combining the domain adapted language models with the CCG-augmented HPB system gave us the best translations for both language pairs providing statistically significant improvements of 6.09 absolute BLEU points (25.94% relative) and 1.69 absolute BLEU points (15.89% relative) over the unadapted PBSMT baselines for the Arabic-English and Chinese-English language pairs, respectively.

2010

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Mitigating Problems in Analogy-based EBMT with SMT and vice versa: A Case Study with Named Entity Transliteration
Sandipan Dandapat | Sara Morrissey | Sudip Kumar Naskar | Harold Somers
Proceedings of the 24th Pacific Asia Conference on Language, Information and Computation

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MATREX: The DCU MT System for WMT 2010
Sergio Penkale | Rejwanul Haque | Sandipan Dandapat | Pratyush Banerjee | Ankit K. Srivastava | Jinhua Du | Pavel Pecina | Sudip Kumar Naskar | Mikel L. Forcada | Andy Way
Proceedings of the Joint Fifth Workshop on Statistical Machine Translation and MetricsMATR

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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

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Combining Multi-Domain Statistical Machine Translation Models using Automatic Classifiers
Pratyush Banerjee | Jinhua Du | Baoli Li | Sudip Naskar | Andy Way | Josef van Genabith
Proceedings of the 9th Conference of the Association for Machine Translation in the Americas: Research Papers

This paper presents a set of experiments on Domain Adaptation of Statistical Machine Translation systems. The experiments focus on Chinese-English and two domain-specific corpora. The paper presents a novel approach for combining multiple domain-trained translation models to achieve improved translation quality for both domain-specific as well as combined sets of sentences. We train a statistical classifier to classify sentences according to the appropriate domain and utilize the corresponding domain-specific MT models to translate them. Experimental results show that the method achieves a statistically significant absolute improvement of 1.58 BLEU (2.86% relative improvement) score over a translation model trained on combined data, and considerable improvements over a model using multiple decoding paths of the Moses decoder, for the combined domain test set. Furthermore, even for domain-specific test sets, our approach works almost as well as dedicated domain-specific models and perfect classification.

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Supertags as Source Language Context in Hierarchical Phrase-Based SMT
Rejwanul Haque | Sudip Naskar | Antal van den Bosch | Andy Way
Proceedings of the 9th Conference of the Association for Machine Translation in the Americas: Research Papers

Statistical machine translation (SMT) models have recently begun to include source context modeling, under the assumption that the proper lexical choice of the translation for an ambiguous word can be determined from the context in which it appears. Various types of lexical and syntactic features have been explored as effective source context to improve phrase selection in SMT. In the present work, we introduce lexico-syntactic descriptions in the form of supertags as source-side context features in the state-of-the-art hierarchical phrase-based SMT (HPB) model. These features enable us to exploit source similarity in addition to target similarity, as modelled by the language model. In our experiments two kinds of supertags are employed: those from lexicalized tree-adjoining grammar (LTAG) and combinatory categorial grammar (CCG). We use a memory-based classification framework that enables the efficient estimation of these features. Despite the differences between the two supertagging approaches, they give similar improvements. We evaluate the performance of our approach on an English-to-Dutch translation task, and report statistically significant improvements of 4.48% and 6.3% BLEU scores in translation quality when adding CCG and LTAG supertags, respectively, as context-informed features.

2009

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Using Supertags as Source Language Context in SMT
Rejwanul Haque | Sudip Kumar Naskar | Yanjun Ma | Andy Way
Proceedings of the 13th Annual conference of the European Association for Machine Translation

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Dependency Relations as Source Context in Phrase-Based SMT
Rejwanul Haque | Sudip Kumar Naskar | Antal van den Bosch | Andy Way
Proceedings of the 23rd Pacific Asia Conference on Language, Information and Computation, Volume 1

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Experiments on Domain Adaptation for English–Hindi SMT
Rejwanul Haque | Sudip Kumar Naskar | Josef van Genabith | Andy Way
Proceedings of the 23rd Pacific Asia Conference on Language, Information and Computation, Volume 2

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English-Hindi Transliteration Using Context-Informed PB-SMT: the DCU System for NEWS 2009
Rejwanul Haque | Sandipan Dandapat | Ankit Kumar Srivastava | Sudip Kumar Naskar | Andy Way
Proceedings of the 2009 Named Entities Workshop: Shared Task on Transliteration (NEWS 2009)

2008

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Bengali, Hindi and Telugu to English Ad-hoc Bilingual Task
Sivaji Bandyopadhyay | Tapabrata Mondal | Sudip Kumar Naskar | Asif Ekbal | Rejwanul Haque | Srinivasa Rao Godavarthy
Proceedings of the 2nd workshop on Cross Lingual Information Access (CLIA) Addressing the Information Need of Multilingual Societies

2007

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JU-SKNSB: Extended WordNet Based WSD on the English All-Words Task at SemEval-1
Sudip Kumar Naskar | Sivaji Bandyopadhyay
Proceedings of the Fourth International Workshop on Semantic Evaluations (SemEval-2007)

2006

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A Modified Joint Source-Channel Model for Transliteration
Asif Ekbal | Sudip Kumar Naskar | Sivaji Bandyopadhyay
Proceedings of the COLING/ACL 2006 Main Conference Poster Sessions

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Handling of Prepositions in English to Bengali Machine Translation
Sudip Kumar Naskar | Sivaji Bandyopadhyay
Proceedings of the Third ACL-SIGSEM Workshop on Prepositions

2005

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A Phrasal EBMT System for Translating English to Bengali
Sudip Kumar Naskar | Sivaji Bandyopadhyay
Proceedings of Machine Translation Summit X: Posters

The present work describes a Phrasal Example Based Machine Translation system from English to Bengali that identifies the phrases in the input through a shallow analysis, retrieves the target phrases using a Phrasal Example base and finally combines the target language phrases employing some heuristics based on the phrase ordering rules for Bengali. The paper focuses on the structure of the noun, verb and prepositional phrases in English and how these phrases are realized in Bengali. This study has an effect on the design of the phrasal Example Base and recombination rules for the target language phrases.

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Use of Machine Translation in India: Current Status
Sudip Naskar | Sivaji Bandyopadhyay
Proceedings of Machine Translation Summit X: Posters

A survey of the machine translation systems that have been developed in India for translation from English to Indian languages and among Indian languages reveals that the MT softwares are used in field testing or are available as web translation service. These systems are also used for teaching machine translation to the students and researchers. Most of these systems are in the English-Hindi or Indian language-Indian language domain. The translation domains are mostly government documents/reports and news stories. There are a number of other MT systems that are at their various phases of development and have been demonstrated at various forums. Many of these systems cover other Indian languages beside Hindi.