Korean-Vietnamese Neural Machine Translation System With Korean Morphological Analysis and Word Sense Disambiguation

Nguyen, Quang-Phuoc; Vo, Anh-Dung; Shin, Joon-Choul; Tran, Phuoc; Ock, Cheol-Young

doi:10.1109/access.2019.2902270

Cited by 27 publications

(23 citation statements)

References 39 publications

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“…However, research papers about Korean-Vietnamese MT remains rare. In this paper, we extend our previous research [1] by applying NER and POS to the Vietnamese corpus as a pre-processing step to improve the performance of Korean-Vietnamese NMT systems.…”

Section: Introductionmentioning

confidence: 90%

“…When conducting experiments, we used our Korean-Vietnamese corpus, which has more than 454K sentence pairs [1], to train the MT models. This corpus is the state-ofthe-art and greatest parallel corpus for Vietnamese-Korean translation systems.…”

Section: Datasetsmentioning

confidence: 99%

“…Whereas the segmented sentence tool created a new corpus with more words than the original corpus, the MA and WSD generated a new corpus with fewer words than the original corpus. More details are given in our previous paper [1].…”

Section: Datasetsmentioning

confidence: 99%

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Korean-Vietnamese Neural Machine Translation with Named Entity Recognition and Part-of-Speech Tags

Nguyen

et al. 2020

IEICE Trans. Inf. & Syst.

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Since deep learning was introduced, a series of achievements has been published in the field of automatic machine translation (MT). However, Korean-Vietnamese MT systems face many challenges because of a lack of data, multiple meanings of individual words, and grammatical diversity that depends on context. Therefore, the quality of Korean-Vietnamese MT systems is still sub-optimal. This paper discusses a method for applying Named Entity Recognition (NER) and Part-of-Speech (POS) tagging to Vietnamese sentences to improve the performance of Korean-Vietnamese MT systems. In terms of implementation, we used a tool to tag NER and POS in Vietnamese sentences. In addition, we had access to a Korean-Vietnamese parallel corpus with more than 450K paired sentences from our previous research paper. The experimental results indicate that tagging NER and POS in Vietnamese sentences can improve the quality of Korean-Vietnamese Neural MT (NMT) in terms of the BiLingual Evaluation Understudy (BLEU) and Translation Error Rate (TER) score. On average, our MT system improved by 1.21 BLEU points or 2.33 TER scores after applying both NER and POS tagging to the Vietnamese corpus. Due to the structural features of language, the MT systems in the Korean to Vietnamese direction always give better BLEU and TER results than translation machines in the reverse direction.

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Section: Introductionmentioning

confidence: 90%

Section: Datasetsmentioning

confidence: 99%

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Korean-Vietnamese Neural Machine Translation with Named Entity Recognition and Part-of-Speech Tags

Nguyen

et al. 2020

IEICE Trans. Inf. & Syst.

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“…The Moses MT used statistical models, and the N-gram of the language model was set to four [17,44]. OpenNMT systems utilized an encoder-decoder model with the following typical attributes: recurrent neural networks = 2 × 512, word-embedding dimension = 512, and input feed = 13 epochs [45,46]. We conducted those SMT and NMT systems for bidirectional translation (i.e., Korean-to-English and English-to-Korean).…”

Section: Experimentationmentioning

confidence: 99%

UPC: An Open Word-Sense Annotated Parallel Corpora for Machine Translation Study

Nguyen

Shin

et al. 2020

Applied Sciences

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Machine translation (MT) has recently attracted much research on various advanced techniques (i.e., statistical-based and deep learning-based) and achieved great results for popular languages. However, the research on it involving low-resource languages such as Korean often suffer from the lack of openly available bilingual language resources. In this research, we built the open extensive parallel corpora for training MT models, named Ulsan parallel corpora (UPC). Currently, UPC contains two parallel corpora consisting of Korean-English and Korean-Vietnamese datasets. The Korean-English dataset has over 969 thousand sentence pairs, and the Korean-Vietnamese parallel corpus consists of over 412 thousand sentence pairs. Furthermore, the high rate of homographs of Korean causes an ambiguous word issue in MT. To address this problem, we developed a powerful word-sense annotation system based on a combination of sub-word conditional probability and knowledge-based methods, named UTagger. We applied UTagger to UPC and used these corpora to train both statistical-based and deep learning-based neural MT systems. The experimental results demonstrated that using UPC, high-quality MT systems (in terms of the Bi-Lingual Evaluation Understudy (BLEU) and Translation Error Rate (TER) score) can be built. Both UPC and UTagger are available for free download and usage.

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“…mined by the structural features of the sentence. In particular, to commercialize the results of WSD, it will be necessary to address most words and their senses in a wide range of domains; these will range from information retrieval [41], [45], [49] or machine translation [4], [14], [30], [37] to even second language education [7], [50], using various sources such as movies and books.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Sense Classification Method for Large-Scale Word Sense Disambiguation

2020

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Word sense disambiguation (WSD) is a task of determining a reasonable sense of a word in a particular context. Although recent studies have demonstrated some progress in the advancement of neural language models, the scope of research is still such that the senses of several words can only be determined in a few domains. Therefore, it is necessary to move toward developing a highly scalable process that can address a lot of senses occurring in various domains. This paper introduces a new large WSD dataset that is automatically constructed from the Oxford Dictionary, which is widely used as a standard source for the meaning of words. We propose a new WSD model that individually determines the sense of the word in accordance with its part of speech in the context. In addition, we introduce a hybrid sense prediction method that separately classifies the less frequently used senses for achieving a reasonable performance. We have conducted comparative experiments to demonstrate that the proposed method is more reliable compared with the baseline approaches. Also, we investigated the adaptation of the method to a realistic environment with the use of news articles. INDEX TERMS Computational and artificial intelligence, English vocabulary learning, natural language processing, neural networks, word sense disambiguation. YOONSEOK HEO received the B.S. and M.S. degrees in computer science (major in in natural language generation) from Sogang Unversity. He is currently pursuing the Ph.D. degree with the Department of Computer Science, Sogang University. He worked as a Researcher with Gachon University, in 2018. He is interested in spoken dialogue system, machine translation, question answering, machine reading comprehension, and named entity recognition. His current research focuses on the way of exploiting multimodal resources for machine translation and addressing large-scale open domain texts for machine reading comprehension.

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Korean-Vietnamese Neural Machine Translation System With Korean Morphological Analysis and Word Sense Disambiguation

Cited by 27 publications

References 39 publications

Korean-Vietnamese Neural Machine Translation with Named Entity Recognition and Part-of-Speech Tags

Korean-Vietnamese Neural Machine Translation with Named Entity Recognition and Part-of-Speech Tags

UPC: An Open Word-Sense Annotated Parallel Corpora for Machine Translation Study

Hybrid Sense Classification Method for Large-Scale Word Sense Disambiguation

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