Towards Sinhala Tamil machine translation

Pushpananda, Randil; Weerasinghe, Ruvan; Niranjan, Mahesan

doi:10.1109/icter.2013.6761202

Cited by 5 publications

(3 citation statements)

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“…In the year 2014, an SMT Framework for SI-TA MT System has been created by Pushpananda, Weerasinghe, and Niranjan (2014). In their work, the result of SMT-dependent translation between language pairs, including TA-SI and SI-TA has been shown.…”

Section: Related Workmentioning

confidence: 99%

Statistical machine translation for Indic languages

Das,

Panda,

Mishra

et al. 2024

Nat. lang. processing

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Statistical Machine Translation (SMT) systems use various probabilistic and statistical Natural Language Processing (NLP) methods to automatically translate from one language to another language while retaining the originality of the context. This paper aims to discuss the development of bilingual SMT models for translating English into fifteen low-resource Indic languages (ILs) and vice versa. The process to build the SMT model is described and explained using a workflow diagram. Samanantar and OPUS corpus are utilized for training, and Flores200 corpus is used for fine-tuning and testing purposes. The paper also highlights various preprocessing methods used to deal with corpus noise. The Moses open-source SMT toolkit is being investigated for the system’s development. The impact of distance-based reordering and Morpho-syntactic Descriptor Bidirectional Finite-State Encoder (msd-bidirectional-fe) reordering on ILs is compared in the paper. This paper provides a comparison of SMT models with Neural Machine Translation (NMT) for ILs. All the experiments assess the translation quality using standard metrics such as BiLingual Evaluation Understudy, Rank-based Intuitive Bilingual Evaluation Score, Translation Edit Rate, and Metric for Evaluation of Translation with Explicit Ordering. From the result, it is observed that msd-bidirectional-fe reordering performs better than the distance-based reordering model for ILs. It is also noticed that even though the IL-English and English-IL systems are trained using the same corpus, the former performs better for all the evaluation metrics. The comparison between SMT and NMT shows that across various languages, SMT performs better in some cases, while NMT outperforms in others.

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Section: Related Workmentioning

confidence: 99%

Statistical machine translation for Indic languages

Das,

Panda,

Mishra

et al. 2024

Nat. lang. processing

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“…Sinhala belongs to the Indo Aryan language family, and Tamil belongs to the Dravidian [24]. Both Sinhala and Tamil have a broad morphological vocabulary: There are 110 noun word forms and 282 verb word forms in Sinhala [24], and Tamil has about 40 nominal vocabulary forms and 240 verb forms. Syntactically, the two languages are also close.…”

Section: Research In Sinhala Tamil Languagesmentioning

confidence: 99%

Enhancing Neural Machine Translation for the Sinhala-Tamil Language Pair with Limited Resources

Pramodya,

Mahima,

Pushpananda

et al. 2024

Int J on Adv. in ICT for Emerging Countries

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Neural Machine Translation has emerged as a promising approach for language translation. Transformer-based deep learning architectures have also significantly enhanced translation performance across various language pairs. However, several language pairs with limited resources face challenges in adopting Neural Machine Translation because of their data requirements. This study investigates methods for expanding the parallel corpus to enhance translation quality.We establish a series of effective guidelines for enhancing Tamil-to-Sinhala machine translation based on cutting-edge Neural Machine Translation techniques like fine-tuning hyperparameters and data augmentation through both forward and backward translation. We validate our methods empirically using standard evaluation metrics. Based on our conducted experiments, we observed that Neural Machine Translation models trained on larger sets of back-translated data outperform other methods of synthetic data generation in Transformer-based training settings. We investigated if we could effectively use the Transformer architecture in the limited-resource context of translating Tamil to Sinhala. Our research demonstrated that Transformer models can surpass the top Statistical Machine Translation models, even in language pairs with limited resources. We achieved an improvement of 3.43 BLEU points in translation quality compared to the statistical translation models.

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“…The attempts by Weerasinghe [228] and Sripirakas et al [229] were also focused on statistical machine translation while Jeyakaran and Weerasinghe [230] attempted a kernel regression method. A yet another attempt was made by Pushpananda et al [231] which they later extended with some quality improvements [232]. An attempt on real-time direct translation between Sinhala and Tamil was done by Rajpirathap et al [233].…”

Section: Translatorsmentioning

confidence: 99%

Survey on Publicly Available Sinhala Natural Language Processing Tools and Research

Silva¹

2019

Preprint

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Sinhala is the native language of the Sinhalese people who make up the largest ethnic group of Sri Lanka. The language belongs to the globe-spanning language tree, Indo-European. However, due to poverty in both linguistic and economic capital, Sinhala, in the perspective of Natural Language Processing tools and research, remains a resource-poor language which has neither the economic drive its cousin English has nor the sheer push of the law of numbers a language such as Chinese has. A number of research groups from Sri Lanka have noticed this dearth and the resultant dire need for proper tools and research for Sinhala natural language processing. However, due to various reasons, these attempts seem to lack coordination and awareness of each other. The objective of this paper is to fill that gap of a comprehensive literature survey of the publicly available Sinhala natural language tools and research so that the researchers working in this field can better utilize contributions of their peers. As such, we shall be uploading this paper to arXiv and perpetually update it periodically to reflect the advances made in the field.

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Towards Sinhala Tamil machine translation

Cited by 5 publications

References 0 publications

Statistical machine translation for Indic languages

Statistical machine translation for Indic languages

Enhancing Neural Machine Translation for the Sinhala-Tamil Language Pair with Limited Resources

Survey on Publicly Available Sinhala Natural Language Processing Tools and Research

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