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Dialect MT: a case study between Cantonese and Mandarin

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ACL '98/COLING '98: Proceedings of the 36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics - Volume 2

Pages 1460 - 1464

https://doi.org/10.3115/980691.980807

Published: 10 August 1998 Publication History

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Abstract

Machine Translation (MT) need not be confined to inter-language activities. In this paper, we discuss inter-dialect MT in general and Cantonese-Mandarin MT in particular. Mandarin and Cantonese are two most important dialects of Chinese. The former is the national lingua franca and the latter is the most influential dialect in South China, Hong Kong and overseas. The difference in between is such that mutual intelligibility is impossible. This paper presents, from a computational point of view, a comparative study of Mandarin and Cantonese at the three aspects of sound systems, grammar rules and vocabulary contents, followed by a discussion of the design and implementation of a dialect MT system between them.

References

[1]

Fromkin V. and Rodman R. (1993) An Introduction to Language (5th edition). Harcourt Brace Jovanovich College Publishers, Orlando, Florida, USA., p. 276.]]

Digital Library

Google Scholar

[2]

Li X., Huang J., Shi Q., Mai Y. and Chen D. (1995) Guangzhou Fangyan Janjiu (Research in Cantonese Dialect). Guangdong People's Press, Guangzhou, China, p. 236.]]

Google Scholar

[3]

LICASS (Language Institute, the Chinese Academy of Social Sciences) (1996) Xiandai Hanyu Cidian (Contemporary Chinese Dictionary). Commercial Press, Beijing, China.]]

Google Scholar

[4]

LSHK (1997) Yueyu Pinyin Zibiao (The Chinese Character List with Cantonese Pinyin). Linguistic Society of Hong Kong, Hong Kong.]]

Google Scholar

[5]

Mai Y. and Tang B. (1997) Shiyong Guangzhouhua Fenlei Cidian (A Practical Semantically-Classified Dictionary of Cantonese). Guandong People's Press, Guangzhou, China.]]

Google Scholar

[6]

Martin K. (1997a) The proper place of men and machines in language translation. Machine Translation, 1--2/12, pp. 3--23.]]

Digital Library

Google Scholar

[7]

Martin K. (1997b) It's still the proper place. Machine Translation, 1--2/12, pp. 35--38.]]

Digital Library

Google Scholar

[8]

Nirenburg S., Carbonell J., Tomita M. and Goodman K. (1992) Machine Translation: A Knowledge-Based Approach. Morgan Kaufmann Publishers, San Mateo, California, USA.]]

Digital Library

Google Scholar

[9]

Sinclair J. (1991) Corpus, Concordance and Collocation. Collins, London, UK.]]

Google Scholar

[10]

Yuan J. (1989) Hanyu Fangyan Gaiyao (Introduction to Chinese Dialects). Wenzi Gaige Press, Beijing, China.]]

Google Scholar

[11]

Zeng Z. F.(1984) Guangzhouhua-Putonghua Kouyuci Duiyi Shouce (A Translation Manual of Cantonese-Mandarin Spoken Words and Phrases). Joint Publishing, Hong Kong.]]

Google Scholar

[12]

Zhang X. and Lau C. F. (1996) Chinese inter-dialect machine translation on the Web. In "Collaboration via the Virtual Orient Express: Proceedings of the Asia-Pacific World Wide Web Conference" S. Mak, F. Castro & J. Bacon-Shone, ed., Hong Kong University, pp. 419--429.]]

Google Scholar

[13]

Zhang X. and Lu F.(1997) Intelligent Chinese pinyin-character conversion based on phrase analysis and dynamic semantic collocation. In "Language Engineering", L. Chen and Q. Yuan, ed., Tsinghua University Press, Beijing, China, pp. 389--395.]]

Google Scholar

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Wushouer MLin DIshida THirayama K(2015)A Constraint Approach to Pivot-Based Bilingual Dictionary InductionACM Transactions on Asian and Low-Resource Language Information Processing10.1145/272314415:1(1-26)Online publication date: 21-Nov-2015
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Wang PNakov PNg HTsujii JHenderson JPasca M(2012)Source language adaptation for resource-poor machine translationProceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning10.5555/2390948.2390984(286-296)Online publication date: 12-Jul-2012
https://dl.acm.org/doi/10.5555/2390948.2390984
Salloum WHabash NJancsary JNeubarth FTrost H(2011)Dialectal to standard Arabic paraphrasing to improve Arabic-English statistical machine translationProceedings of the First Workshop on Algorithms and Resources for Modelling of Dialects and Language Varieties10.5555/2140533.2140535(10-21)Online publication date: 31-Jul-2011
https://dl.acm.org/doi/10.5555/2140533.2140535
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Dialect MT: a case study between Cantonese and Mandarin

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ACL '98/COLING '98: Proceedings of the 36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics - Volume 2

August 1998

768 pages

Program Chairs:
Christian Boitet
Université Joseph Fourier, Grenoble, France
,
Pete Whitelock
Sharp Laboratories of Europe Ltd., Oxford, United Kingdom

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Association for Computational Linguistics

United States

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Published: 10 August 1998

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Wushouer MLin DIshida THirayama K(2015)A Constraint Approach to Pivot-Based Bilingual Dictionary InductionACM Transactions on Asian and Low-Resource Language Information Processing10.1145/272314415:1(1-26)Online publication date: 21-Nov-2015
https://dl.acm.org/doi/10.1145/2723144
Wang PNakov PNg HTsujii JHenderson JPasca M(2012)Source language adaptation for resource-poor machine translationProceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning10.5555/2390948.2390984(286-296)Online publication date: 12-Jul-2012
https://dl.acm.org/doi/10.5555/2390948.2390984
Salloum WHabash NJancsary JNeubarth FTrost H(2011)Dialectal to standard Arabic paraphrasing to improve Arabic-English statistical machine translationProceedings of the First Workshop on Algorithms and Resources for Modelling of Dialects and Language Varieties10.5555/2140533.2140535(10-21)Online publication date: 31-Jul-2011
https://dl.acm.org/doi/10.5555/2140533.2140535
Paul MFinch ASumita E(2011)Word segmentation for dialect translationProceedings of the 12th international conference on Computational linguistics and intelligent text processing - Volume Part II10.5555/1964750.1964756(55-67)Online publication date: 20-Feb-2011
https://dl.acm.org/doi/10.5555/1964750.1964756

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Automatic speech recognition for mixed dialect utterances by mixing dialect language models

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Automatic speech recognition for mixed dialect utterances by mixing dialect language models

Automatic Dialect Identification: A Study of British English

Automatic dialect classification: advances for read and spontaneous speech, and printed text

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