Standardizing Sinhala Code-Mixed Text using Dictionary based Approach

Kugathasan, Archchana; Sumathipala, Sagara

doi:10.1109/icip48927.2020.9367353

Cited by 3 publications

(3 citation statements)

References 17 publications

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“…Together it is understood as the word 'photo'. The research study of Kugathasan and Sumathipala (2020) clearly explains the challenges in Sinhala-English code-mixed texts.…”

Section: Introductionmentioning

confidence: 90%

“…In the parallel corpus SECM is considered the source sentence and Sinhala is considered the target sentence. The human translator was advised to follow the Singlish to Sinhala mapping provided in the research study of Kugathasan and Sumathipala (2020) as the guide to maintain the consistency in the translation. The corpus contains around 1500 parallel sentences of SECM and it's translated Sinhala sentences.…”

Section: Secm Text and Corpus Creationmentioning

confidence: 99%

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Neural Machine Translation for Sinhala-English Code-Mixed Text

Kugathasan

Sumathipala

2021

Proceedings of the Conference Recent Advances in Natural Language Processing - Deep Learning for Natural Language Processing Me

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“…Together it is understood as the word 'photo'. The research study of Kugathasan and Sumathipala (2020) clearly explains the challenges in Sinhala-English code-mixed texts.…”

Section: Introductionmentioning

confidence: 90%

Section: Secm Text and Corpus Creationmentioning

confidence: 99%

Neural Machine Translation for Sinhala-English Code-Mixed Text

Kugathasan

Sumathipala

2021

Proceedings of the Conference Recent Advances in Natural Language Processing - Deep Learning for Natural Language Processing Me

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“…After the extraction process, each SCM sentence in the corpus is human translated into Sinhala sentences with the help of a human translator, who is a Sinhala native speaker. The translator followed the mapping proposed in the research study of Kugathasan and Sumathipala et al (2020) [48] for the manual human translation process. Thus, the SCM sentence is the source sentence, and the Sinhala sentence is the target sentence.…”

Section: Corpus Creationmentioning

confidence: 99%

Neural Machine Translation for Sinhala-English Code-Mixed Text

Kugathasan¹,

Sumathipala²

2022

Int J on Adv. in ICT for Emerging Countries

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Multilingual societies use a mix of two or more languages when communicating. It has become a famous way of communication in social media in South Asian communities. Sinhala-English Code-Mixed Texts (SCMT) are known as the most popular text representation used in Sri Lanka in the informal context such as social media chats, comments, small talks etc. The challenges in utilizing the SCMT sentences are addressed in this paper. The main focus of this study is translating code-mixed sentences written in Sinhala-English to the standard Sinhala language. Since Sinhala is a low-resource language, we were able to collect only a limited number of SCMT-Sinhala parallel sentences. Creating the parallel corpus of SCMT-Sinhala was a time-consuming and costly task. The proposed architecture of Neural Machine Translation(NMT) to translate SCMT text to Sinhala, is built with a combination of normalization pipeline, Long Short Term Memory(LSTM) units, Sequence to Sequence(Seq2Seq) and Teachers Forcing mechanism. The proposed model is evaluated against the current state-of-the-art models using the same experimental setup, which proves the Teacher Forcing Algorithm combined with Seq2Seq and Normalization improves the quality of the translation. The predicted outputs from the model are compared using the BLEU (Bilingual Evaluation Understudy) metric and our proposed model achieved a better BLEU score of 33.89 in the evaluation.

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