Punjabi to English Machine Transliteration for Proper Nouns

Kaur, Arshveer; Goyal, Vishal

doi:10.1109/iot-siu.2018.8519877

Cited by 6 publications

(3 citation statements)

References 5 publications

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“…The transliteration system was developed for the transliteration of name entity and some technical terms [13]- [15]. But now, it is used (i) corpus / data acquisition for resource-scarce language, (ii) to remove communication barrier for non-native language reader, (iii) for different applications such as information retrieval, text summarization, opinion mining, etc.…”

Section: B Results Comparison With State-of-art Techniquementioning

confidence: 99%

GRT: Gurmukhi to Roman Transliteration System using Character Mapping and Handcrafted Rules

Singh¹,

Sachan²

2019

IJITEE

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In the last two decades, the transliteration system has got significant research attention. It is observed that Punjabi to English transliteration for all type of part-of-speech words is comparably less studied. Currently, some research work in this area is carried out but only for proper nouns and some technical terms. So, there is need to focus on all type of words. The Gurmukhi to Roman transliteration (GRT) system is the first proposed system for transliteration of all kind of part-of-speech words. This system uses the handcrafted-rules and character mapping (CM) approach for transliteration between languages involved. The CM is done for Gurmukhi script with its equivalent to Roman script. It transliterates text written in Punjabi language into English language. It is tested on 65,130 Punjabi words and achieved accuracy of 99.27%, which is better than other state-of-art system results. The developed system can be used in social media text normalization, translation, sentiment analysis of multilingual text, text summarization of multilingual text, etc.

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Section: B Results Comparison With State-of-art Techniquementioning

confidence: 99%

GRT: Gurmukhi to Roman Transliteration System using Character Mapping and Handcrafted Rules

Singh¹,

Sachan²

2019

IJITEE

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show abstract

“…Moreover, Kaur and Singh (2015) introduced an algorithm for translating handwritten text into the International Phonetic Alphabet (IPA), effectively shedding light on the limitations of grapheme-based transliteration. Notably, Kaur and Goyal (2018) presented a remarkably accurate Punjabi-to-English transliteration method, employing SMT techniques that achieved an impressive accuracy rate of 96% across various source-target language combinations. Lastly, Hany Hassan et al (2018) discussed the achievements of Microsoft's MT system, demonstrating its parity with professional human translations for Chinese-English language pairs and its superior performance in comparison to crowdsourced references.…”

Section: Literature Reviewmentioning

confidence: 99%

Improving neural machine translation by integrating transliteration for low-resource English–Assamese language

Nath,

Sarkar,

Mukhopadhyay

et al. 2024

NLP

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In machine translation (MT), one of the challenging tasks is to translate the proper nouns and technical terms from the source language to the target language while preserving the phonetic equivalent of original term. Machine transliteration, an essential part of MT systems, plays a vital role in handling proper nouns and technical terms. In this paper, a hybrid attention-based encoder–decoder machine transliteration system is proposed for the low-resource English to the Assamese language. In this work, the proposed machine transliteration system is integrated with the previously published hybrid attention-based encoder–decoder neural MT model to improve the translation quality of English to the Assamese language. The proposed integrated MT system demonstrated good results across various performance metrics such as BLEU, sacreBLEU, METEOR, chrF, RIBES, and TER for English to Assamese translation. Additionally, human evaluation was also conducted to assess translation quality. The proposed integrated MT system was compared with two existing systems: the Bing translation service model and the Samanantar Indic translation model.

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“…For the research community free sample data-set of 5000+ token is available for reference at English Punjabi Aligned Nouns Dataset -Mendeley Data [33] and it is increased on a monthly basis. This is the first of its kind initiative for creating a comparable corpus for the English and Punjabi language pair and making it available to research community freely.…”

Section: E Extraction Of Nouns From the Bilingual Corpusmentioning

confidence: 99%

Building English – Punjabi Aligned Parallel Corpora of Nouns from Comparable Corpora

Kaur,

Singh

2023

Applied Computer Systems

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Comparable corpora are the right resources for extracting parallel data due to their abundant availability. It is of great importance where parallel data are scarce. In this study, the focus is placed on building of parallel data for Punjabi and English language pair. The raw data were collected from web contents of “Mann Ki Baat”, which is a collection of textual speeches of Prime Minister of India Mr. Narendra Modi broadcasted every last Sunday of the month. Data were cleaned and pre-processed using a natural language toolkit. An alignment model using BERT was built that aligned two textual files on a sentence level. Furthermore, extraction of noun forms with the help of NLTK library in Python programming was performed. The noun aligned dataset was built for English-Punjabi language pair and made available at Mendeley data repository.

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Punjabi to English Machine Transliteration for Proper Nouns

Cited by 6 publications

References 5 publications

GRT: Gurmukhi to Roman Transliteration System using Character Mapping and Handcrafted Rules

GRT: Gurmukhi to Roman Transliteration System using Character Mapping and Handcrafted Rules

Improving neural machine translation by integrating transliteration for low-resource English–Assamese language

Building English – Punjabi Aligned Parallel Corpora of Nouns from Comparable Corpora

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