Zero-shot North Korean to English Neural Machine Translation by Character Tokenization and Phoneme Decomposition

Kim, Hwichan; Hirasawa, Tosho; Komachi, Mamoru

doi:10.18653/v1/2020.acl-srw.11

Cited by 2 publications

(1 citation statement)

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“…In relation to Korean in this regard, the concept of detailed segmentation intrigued many researchers in NLP in general (Park et al 2018;Kim et al 2020;Yongseok and Lee 2020;Park et al 2020), for a Korean word had a large volume of affixes and morphemes. In MT in specific, various combinations of token units were suggested.…”

Section: Related Workmentioning

confidence: 99%

Guidance to Pre-tokeniztion for SacreBLEU: Meta-Evaluation in Korean

Kim¹,

Kim²

2022

Preprint

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SacreBLEU, by incorporating a text normalizing step in the pipeline, has been well-received as an automatic evaluation metric in recent years. With agglutinative languages such as Korean, however, the metric cannot provide a conceivable result without the help of customized pre-tokenization. In this regard, this paper endeavors to examine the influence of diversified pre-tokenization schemes –word, morpheme, character, and subword– on the aforementioned metric by performing a meta-evaluation with manually-constructed into-Korean human evaluation data. Our empirical study demonstrates that the correlation of SacreBLEU (to human judgment) fluctuates consistently by the token type. The reliability of the metric even deteriorates due to some tokenization, and MeCab is not an exception. Guiding through the proper usage of tokenizer for each metric, we stress the significance of a character level and the insignificance of a Jamo level in MT evaluation.

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Section: Related Workmentioning

confidence: 99%

Guidance to Pre-tokeniztion for SacreBLEU: Meta-Evaluation in Korean

Kim¹,

Kim²

2022

Preprint

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The boundaries of meaning: a case study in neural machine translation

Balashov

2022

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The success of deep learning in natural language processing raises intriguing questions about the nature of linguistic meaning and ways in which it can be processed by natural and artificial systems. One such question has to do with subword segmentation algorithms widely employed in language modeling, machine translation, and other tasks since 2016. These algorithms often cut words into semantically opaque pieces, such as 'period', 'on', 't', and 'ist' in 'period|on|t|ist'. The system then represents the resulting segments in a dense vector space, which is expected to model grammatical relations among them. This representation may in turn be used to map 'period|on|t|ist' (English) to 'par|od|ont|iste' (French). Thus, instead of being modeled at the lexical level, translation is reformulated more generally as the task of learning the best bilingual mapping between the sequences of subword segments of two languages; and sometimes even between pure character sequences: 'p|e|r|i|o|d|o|n|t|i|s|t|' → 'p|a|r|o|d|o|n|t|i|s|t|e'. Such subword segmentations and alignments are at work in highly efficient end-to-end machine translation systems, despite their allegedly opaque nature. The computational value of such processes is unquestionable. But do they have any linguistic or philosophical plausibility? I attempt to cast light on this question by reviewing the relevant details of the subword segmentation algorithms and by relating them to important philosophical and linguistic debates, in the spirit of making artificial intelligence more transparent and explainable.

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Zero-shot North Korean to English Neural Machine Translation by Character Tokenization and Phoneme Decomposition

Cited by 2 publications

References 13 publications

Guidance to Pre-tokeniztion for SacreBLEU: Meta-Evaluation in Korean

Guidance to Pre-tokeniztion for SacreBLEU: Meta-Evaluation in Korean

The boundaries of meaning: a case study in neural machine translation

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