Performance improvement of Machine Translation system using LID and post-editing

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

doi:10.1109/tencon.2016.7848403

Cited by 2 publications

(2 citation statements)

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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%

“…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). The current work makes use of English‐to‐Tamil and English‐to‐Hindi parallel text corpus from TDIL, MeitY, India (TDIL & MeitY, 2017) which contains parallel sentences from tourism, health, and agriculture domains.…”

Section: Effect Of Punctuation On Nlp Systemsmentioning

confidence: 99%

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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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