Comparing Statistical and Neural Machine Translation Performance on Hindi-to-Tamil and English-to-Tamil

Ramesh, Akshai; Parthasarathy, Venkatesh Balavadhani; Way, Andy; Haque, Rejwanul

doi:10.20944/preprints202012.0580.v1

Cited by 5 publications

(4 citation statements)

References 24 publications

(34 reference statements)

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“…They report that techniques such as domain adaptation and back-translation can make training NMT systems on low-resource languages possible. Similar findings was also reported by Ramesh et al (2020) for Tamil and Dandapat and Federmann (2018) for Telugu .…”

Section: Neural Machine Translationsupporting

confidence: 90%

Optimal Word Segmentation for Neural Machine Translation into Dravidian Languages

Dhar

Bisazza

Noord

2021

Proceedings of the 8th Workshop on Asian Translation (WAT2021)

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Dravidian languages, such as Kannada and Tamil, are notoriously difficult to translate by state-of-the-art neural models. This stems from the fact that these languages are morphologically very rich as well as being lowresourced. In this paper, we focus on subword segmentation and evaluate Linguistically Motivated Vocabulary Reduction (LMVR) against the more commonly used SentencePiece (SP) for the task of translating from English into four different Dravidian languages. Additionally we investigate the optimal subword vocabulary size for each language. We find that SP is the overall best choice for segmentation, and that larger subword vocabulary sizes lead to higher translation quality.

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Section: Neural Machine Translationsupporting

confidence: 90%

Optimal Word Segmentation for Neural Machine Translation into Dravidian Languages

Dhar

Bisazza

Noord

2021

Proceedings of the 8th Workshop on Asian Translation (WAT2021)

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“…A low BLEU score indicates differences in n-grams between the machine translation and the reference translation (i.e., human translation). Similarly, (Ramesh et al, 2020) achieved a notably low BLEU score for English-to-Tamil translation. They argued that the nature of the language contributes to the increased number of n-gram mismatches with the reference translation, despite the translation itself being of good quality.…”

Section: Human Translation As a Referencementioning

confidence: 97%

Cross-lingual Classification of Crisis-related Tweets Using Machine Translation

Al Amer,

Lee,

Smith

2023

Proceedings of the Conference Recent Advances in Natural Language Processing - Large Language Models for Natural Language Proce

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Utilisation of multilingual language models such as mBERT and XLM-RoBERTa has increasingly gained attention in recent work by exploiting the multilingualism of such models in different downstream tasks across different languages. However, performance degradation is expected in transfer learning across languages compared to monolingual performance although it is an acceptable trade-off considering the sparsity of resources and lack of available training data in low-resource languages. In this work, we study the effect of machine translation on the cross-lingual transfer learning in a crisis event classification task. Our experiments include measuring the effect of machine-translating the test data into the source language and vice versa 1 . We evaluated and compared the performance in terms of accuracy and F1-Score. The results show that translating the source data into the target language improves the prediction accuracy by 14.8% and the Weighted Average F1-Score by 19.2% when compared to zero-shot transfer to an unseen language.

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“…LSTM uses memory cells for retaining the values and also requires a minimal amount of training data. Hence, the proposed system overcomes the existing systems by using the NMT model involving GRU LSTM in both the encoding and decoding phases (7)(8)(9) . The proposed work has been implemented using a large number of sentences counted to several thousand.…”

Section: Introductionmentioning

confidence: 99%

Bi-Lingual Machine Translation Approach using Long Short–Term Memory Model for Asian Languages

Prasanna¹,

Latha²

2023

IJST

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Objectives: To develop an appropriate machine translation model for translating text from English to Tamil. Methods: The proposed work uses a Gated Recurrent Unit (GRU) Long Short-Term Memory (LSTM) model. The Repeat Vector function is used for fitting both the decoder and encoder parts of the network model. Adam optimizer is used because of its faster execution and less consumption of memory. It mainly uses the text corpora which are available in the Internet repository namely Technology Development for Indian Languages (TDIL), Linguistic Data Consortium for Indian Languages (LDCIL), Kaggle, and Ishikahooda. Findings: The motivation for the proposed work emerged from identifying the regional language Tamil as one of the less frequently used languages in the existing translation systems. The Tamil Character Set is one of the challenging factors for the existence of fewer such translation systems. The proposed system produces a BLEU score of 0.9, a Meteor score of 0.98, a TER score of 0.5, a WER score of 20%, an Accuracy rate of 5 (in a 5-point grading scale), and an Adequacy rate of 5 (on a 5-point grading scale) which are significantly better than the existing systems. Novelty: The space complexity of the proposed LSTM-based English Tamil Translator is fine-tuned to 256 units of memory using Adam optimizer for achieving less storage consumption. The number of layers is optimized for reducing the execution time. Unicode Transformation Format (UTF-8) encoding is used to incorporate Tamil language characters. This work has been implemented with a wide range of sentences counted to several thousand. LSTM-based English Tamil Translator is helpful for bilingual learners who are learning specifically Tamil language.

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Comparing Statistical and Neural Machine Translation Performance on Hindi-to-Tamil and English-to-Tamil

Cited by 5 publications

References 24 publications

Optimal Word Segmentation for Neural Machine Translation into Dravidian Languages

Optimal Word Segmentation for Neural Machine Translation into Dravidian Languages

Cross-lingual Classification of Crisis-related Tweets Using Machine Translation

Bi-Lingual Machine Translation Approach using Long Short–Term Memory Model for Asian Languages

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