Neural Machine Translation for English-Tamil

Choudhary, Himanshu; Pathak, Aditya; Saha, Rajiv Ratan; Kumaraguru, Ponnurangam

doi:10.18653/v1/w18-6459

Cited by 26 publications

(8 citation statements)

References 12 publications

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“…In works [50][51][52][53][54][55][56][57], CAT tools for translating into Indian languages were presented. In [57], the authors presented an open source and extendable Morphological Analyser cum Generator (MAG) for Tamil language.…”

Section: Related Workmentioning

confidence: 99%

Deep Searching Based Optimized Machine Translation for Language Translation Tool

Velmurugan¹,

Sumathy²,

Pradeep

2022

Preprint

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Fuzzy matching techniques are the presently used methods in translating the words. Neural machine translation and statistical machine translation are the methods used in MT. In machine translator tool, the strategy employed for translation needs to handle large amount of datasets and therefore the performance in retrieving correct matching output can be affected. In order to improve the matching score of MT, the advanced techniques can be presented by modifying the existing fuzzy based translator and neural machine translator. The conventional process of modifying architectures and encoding schemes are tedious process. Similarly the preprocessing of datasets also involves more time consumption and memory utilization. In this paper, a new spider web based searching enhanced translation is presented to be employed with the neural machine translator. The proposed scheme enables deep searching of available dataset to detect the accurate matching result. In addition, the quality of translation is improved by presenting an optimal selection scheme for using the sentence matches in source augmentation. The matches retrieved using various matching scores are applied to an optimization algorithm. The source augmentation using optimal retrieved matches increases the translation quality. Further, the selection of optimal match combination helps to reduce time requirement, since it is not necessary to test all retrieved matches in finding target sentence. The performance of translation is validated by measuring the quality of translation using BLEU score. The results are evaluated and compared with other available NMT methods to validate the work.

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Section: Related Workmentioning

confidence: 99%

Deep Searching Based Optimized Machine Translation for Language Translation Tool

Velmurugan¹,

Sumathy²,

Pradeep

2022

Preprint

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“…It increases the computation time and adds complexity to the model's training. To address this problem, some studies [18,3] applied Byte Pair Encoding (BPE), which tokenizes sentences at subword level [17]. This technique keeps input length to a reasonable level while handling unseen and rare words.…”

Section: Related Workmentioning

confidence: 99%

VSEC: Transformer-based Model for Vietnamese Spelling Correction

Nguyen

Bui

et al. 2021

Preprint

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Spelling error correction is one of topics which have a long history in natural language processing. Although previous studies have achieved remarkable results, challenges still exist. In the Vietnamese language, a state-of-the-art method for the task infers a syllable's context from its adjacent syllables. The method's accuracy can be unsatisfactory, however, because the model may lose the context if two (or more) spelling mistakes stand near each other. In this paper, we propose a novel method to correct Vietnamese spelling errors. We tackle the problems of mistyped errors and misspelled errors by using a deep learning model. The embedding layer, in particular, is powered by the byte pair encoding technique. The sequence to sequence model based on the Transformer architecture makes our approach different from the previous works on the same problem. In the experiment, we train the model with a large synthetic dataset, which is randomly introduced spelling errors. We test the performance of the proposed method using a realistic dataset. This dataset contains 11,202 human-made misspellings in 9,341 different Vietnamese sentences. The experimental results show that our method achieves encouraging performance with 86.8% errors detected and 81.5% errors corrected, which improves the state-of-the-art approach 5.6% and 2.2%, respectively.

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“…Within the context of Indian languages, (Chandola and Mahalanobis, 1994) and (Dave et al, 2001) were one of the first works to explore a rulebased approach for translation from Hindi to English whereas (Patel et al, 2018), (Barman et al, 2014), (Saini and Sahula, 2018) and (Choudhary et al, 2018) have explored this problem through the prism of NMT. (Philip et al, 2019) and (Madaan and Sadat, 2020) extend the concept of multilingual NMT to the setting of Indian languages.…”

Section: Previous Workmentioning

confidence: 99%

Exploring Pair-Wise NMT for Indian Languages

Akella,

Allu,

Ragupathi

et al. 2020

Preprint

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In this paper, we address the task of improving pair-wise machine translation for specific low resource Indian languages. Multilingual NMT models have demonstrated a reasonable amount of effectiveness on resource-poor languages. In this work, we show that the performance of these models can be significantly improved upon by using back-translation through a filtered back-translation process and subsequent fine-tuning on the limited pair-wise language corpora. The analysis in this paper suggests that this method can significantly improve a multilingual models' performance over its baseline, yielding state-of-the-art results for various Indian languages. * *Equal Contribution. Sridhar worked on Tamil and Urdu, Himal worked on Gujarati, Kartheek worked on Malayalam and Marathi, Aman worked on Hindi and Punjabi and Zeeshan worked on Odiya. Himal was responsible for drafting this paper.

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Neural Machine Translation for English-Tamil

Cited by 26 publications

References 12 publications

Deep Searching Based Optimized Machine Translation for Language Translation Tool

Deep Searching Based Optimized Machine Translation for Language Translation Tool

VSEC: Transformer-based Model for Vietnamese Spelling Correction

Exploring Pair-Wise NMT for Indian Languages

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