Analysis on bilingual machine translation systems for English and Tamil

G, Sangavi; Mrinalini, K.; Vijayalakshmi, P.

doi:10.1109/iccpeic.2016.7557203

Cited by 8 publications

(3 citation statements)

References 5 publications

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“…The co‐authors of this article are active researchers in machine translation (Mrinalini et al, 2018; Mrinalini et al, 2022; Sangavi et al, 2016) and thus based on their research and observations, we intend to extend the translation block using the work done in the above‐mentioned articles to enable translation for other Indian languages.…”

Section: Data and Proposed Approachmentioning

confidence: 99%

PooRaa‐Agri KG: An agricultural knowledge graph‐based simplified multilingual query system

Sivakumar,

Srinivasan,

Kannan

et al. 2023

Expert Systems

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The current work proposes PooRaa‐Agri KG, an agricultural knowledge graph‐based simplified multilingual query system that works in real time to provide concise answers for agriculture‐based queries. The proposed approach accommodates real‐time and low‐resource queries in English and Hindi with a novel multi‐stage solution consisting of data pre‐processing, sentence simplification, triplet extraction, knowledge graph generation, sentence reconstruction, query‐to‐reconstructed sentence matching, and machine translation as its sub‐modules. In this work, a novel combination of rule‐based sentence simplification and triplet extraction is carried out resulting in a triplet similarity score of 86.56% for the extracted triplets. This method is superior to the existing triplet extraction method whose triplet similarity score was found to be 60.65%. Further, the proposed work makes use of heuristic rules to reconstruct sentences which when evaluated by human evaluators for meaningfulness and grammar resulted in a score of 3.09/4 and 2.95/4 respectively. To complete end‐to‐end communication in the proposed system, a similarity‐based query answer system is proposed in this work.

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Section: Data and Proposed Approachmentioning

confidence: 99%

PooRaa‐Agri KG: An agricultural knowledge graph‐based simplified multilingual query system

Sivakumar,

Srinivasan,

Kannan

et al. 2023

Expert Systems

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“…In this paper, Statistical Machine Translation (SMT) systems developed by the authors (Mrinalini et al, 2018) for English‐to‐Tamil and English‐to‐Hindi translation are used. It is important to note that the linguistic gap between the source and target languages affects the re‐ordering and alignments of words in the translation model (Mrinalini et al, 2016; Sangavi et al, 2016; Vasuki & Sankaravelayuthan, 2013) thus affecting the SMT output. Apart from the linguistic variations in both the languages, the type of training or testing data influences the performance of the SMT (Mrinalini et al, 2016).…”

Section: Effect Of Punctuation On Nlp Systemsmentioning

confidence: 99%

Feature‐weightedAdaBoostclassifier for punctuation prediction in Tamil and HindiNLPsystems

2021

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Punctuation marks play a vital role in text representation and interpretation, and are useful in enhancing the performance of modern Natural Language Processing (NLP) systems such as voice input typing aids, machine translation, and speech synthesis systems. Punctuation marks, except period, are inherently not available in Indian languages such as Tamil and Hindi. However, some modern forms of writing such as news articles, blogs, stories, and so forth, incorporate user‐defined punctuation marks in these languages. The current work proposes an automatic punctuation prediction system for texts in Tamil and Hindi using classification approach, where punctuation prediction is considered as a multi‐class classification problem. Word‐level text features are chosen and are analysed to validate their language‐dependency and significance towards punctuation prediction. A Feature‐weighted AdaBoost (FAda) classifier is proposed that defines a novel boosting factor to adjust the hypothesis weight of the weak classifiers, hence reducing the number of false classifications. It is observed that the proposed classifier outperforms the other classification techniques such as, AdaBoost, SVM, CART, CRF, and Bi‐LSTM by a maximum difference of 50% and 16% in the macro F1‐scores for Tamil and Hindi texts, respectively. The proposed classifier performs on par with the attention‐based classifier for both Tamil and Hindi texts. Further, as a proof of concept, the proposed punctuation prediction system is applied to voice keyboard, machine translation, and speech synthesis systems, to validate the effect of the punctuation marks on the performance of these Natural Language Processing (NLP) systems.

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“…It concerns to capture syntax and syntactic variation in both translated languages. [7] Basically, in this process, the system receives sentences as an input from the users then continues the process by passing it to the parser, in this term, Stanford parser. It may generate some features that is usually used for translating.…”

Section: B Rule-based Systemmentioning

confidence: 99%

Comparing Rule-Based Translation to Syntax Tree Diagram Translation

Heryono

2018

INISTA

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Machine translation has developed more effectively to be specified as modern linguistics characterization. It indicates the formalization of translating process by machine within humanly approaching. Recently, in order to help people communicate indirectly through written, machine translation has its own system to break and match the codes through comprehensive database of words pocket. In linguistics perspective, machine translation always be assisted by its definite performance through derivative computational system. The problem appears when users need to translate ambiguous sentence from English to Bahasa Indonesia or vice versa. The paper used rule-based system which contains particular parts in order to run the process of translating; starts from tokenization, pre-processing, reordering and morphological process which lead to output accuracy. And used syntactic diagram translation, which was carried out manually by generating sentences into small units. The result of this research showed that RBS runs by standard linguistics system; and the output of translating refer to basic translation without giving any alternative translation nor meaning. While using syntactic diagram, there are possibility to get alternative result not only by linguistics features but also based on signification meaning of both translations. Those techniques run by considering linguistics features in order to help users to get translation output better.

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Analysis on bilingual machine translation systems for English and Tamil

Cited by 8 publications

References 5 publications

PooRaa‐Agri KG: An agricultural knowledge graph‐based simplified multilingual query system

PooRaa‐Agri KG: An agricultural knowledge graph‐based simplified multilingual query system

Feature‐weightedAdaBoostclassifier for punctuation prediction in Tamil and HindiNLPsystems

Comparing Rule-Based Translation to Syntax Tree Diagram Translation

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