ANGLABHARTI: a multilingual machine aided translation project on translation from English to Indian languages

Sinha, R.M.K.; Sivaraman, K.; Agrawal, Anubhav; Jain, Rahul; Srivastava, Rishabh; Jain, Ashu

doi:10.1109/icsmc.1995.538002

Cited by 42 publications

(26 citation statements)

References 1 publication

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“…There are large number of active groups working on MT Some of the active players in MT research are CDAC, IIT Bombay, IISC Bangalore, IIIT Hyderabad, IIT Kanpur, Tamil University, Cochin University, Amrita University. Some of the projects funded by TDIL are -Angalabharthi [4] Research work in MT for Indian languages involves various MT techniques like Rule Based, Empirical Based. Rule based machine translation system retrieves language knowledge from dictionary and grammar of the respective language to aid in translation.…”

Section: Mt In Indiamentioning

confidence: 99%

Exploration of Corpus Augmentation Approach for English-Hindi Bidirectional Statistical Machine Translation System

Jaya¹,

Gupta²

2016

IJECE

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Even though lot of Statistical Machine Translation (SMT) research work is happening for English-Hindi language pair, there is no effort done to standardize the dataset. Each of the research work uses different number of sentences, datasets and parameters during various phases of translation resulting in varied translation output. So comparing these models, understand the result of these models, to get insight into corpus behavior for these models, regenerating the result of these research work becomes tedious. This necessitates the need for standardization of dataset and to identify the common parameter for the development of model. The main contribution of this paper is to introduce an approach to standardize the dataset and to identify the best parameter which in combination gives best performance. It also investigates a novel corpus augmentation approach to improve the translation quality of English-Hindi bidirectional statistical machine translation system. This model works well for the scarce resource without incorporating the external parallel data corpus of the underlying language. This experiment is carried out using Open Source phrase-based toolkit Moses. Indian Languages Corpora Initiative (ILCI) Hindi-English tourism corpus is used. With limited dataset, considerable improvement is achieved using the corpus augmentation approach for the English-Hindi bidirectional SMT system.

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Section: Mt In Indiamentioning

confidence: 99%

Exploration of Corpus Augmentation Approach for English-Hindi Bidirectional Statistical Machine Translation System

Jaya¹,

Gupta²

2016

IJECE

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“…RBMT can be a good system to translate English text to Indian Language [1]. It has rule base and morph analyzer to support many small issues and guide the translation process to go in right direction.…”

Section: Introductionmentioning

confidence: 99%

An approach to integrate translation memory in MT system for English to Indian language

Shukla

Jaiswal

Shukla³

2015

2015 International Conference on Cognitive Computing and Information Processing(CCIP)

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Although there is a requirement for English to Indian language translation, there are no proper and enough tools for the translators to use, because of the complexities involved in it. The research in developing Machine translation systems is going on but they are not yet to the stage of product or useful systems development. Major reason is the word level, phrase level and semantic level translation complexities involved because of the different cultures and use of the languages and non availability of good quality Translation Memories. Translation memories are widely used by translation industry because of their ease of use, ability to create glossaries, terminologies and phrasal dictionaries in a domain. It also helps maintain writing styles throughout the series of documents. Also, Human Machine Interface is better for human translators. On the other hand Machine Translation (MT) systems when tuned properly produce better translations for simpler sentences. Complex and compound sentences are difficult for MT to translate solely by machines. Post editing by human is required. This paper proposes integration of these two methods to get the respective advantages of both the systems providing better translation support systems for translators to work faster without compromising quality and help human translator to maintain proper writing styles intact. The concept was validated with a small experiment using a widely used TM tool (Wordfast) and English to Indian Language Rule Based Machine Translation system provided by IIT Kanpur.

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“…Its beta_version is Angla Hindi for English to Hindi translation and is available at http://anglahindi.iitk.ac.in. [17].…”

Section: Interlingua Based Machine Translationmentioning

confidence: 99%

Machine Translation Systems for Indian Languages

Nair¹,

Peter²

2012

IJCA

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ANGLABHARTI: a multilingual machine aided translation project on translation from English to Indian languages

Cited by 42 publications

References 1 publication

Exploration of Corpus Augmentation Approach for English-Hindi Bidirectional Statistical Machine Translation System

Exploration of Corpus Augmentation Approach for English-Hindi Bidirectional Statistical Machine Translation System

An approach to integrate translation memory in MT system for English to Indian language

Machine Translation Systems for Indian Languages

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