Phuong-Thai Nguyen

Also published as: Phuong Thai Nguyen


Iterative Multilingual Neural Machine Translation for Less-Common and Zero-Resource Language Pairs
Minh Thuan Nguyen | Phuong Thai Nguyen | Van Vinh Nguyen | Minh Cong Nguyen Hoang
Proceedings of the 34th Pacific Asia Conference on Language, Information and Computation

Improving Multilingual Neural Machine Translation For Low-Resource Languages: French, English - Vietnamese
Thi-Vinh Ngo | Phuong-Thai Nguyen | Thanh-Le Ha | Khac-Quy Dinh | Le-Minh Nguyen
Proceedings of the 3rd Workshop on Technologies for MT of Low Resource Languages

Prior works have demonstrated that a low-resource language pair can benefit from multilingual machine translation (MT) systems, which rely on many language pairs’ joint training. This paper proposes two simple strategies to address the rare word issue in multilingual MT systems for two low-resource language pairs: French-Vietnamese and English-Vietnamese. The first strategy is about dynamical learning word similarity of tokens in the shared space among source languages while another one attempts to augment the translation ability of rare words through updating their embeddings during the training. Besides, we leverage monolingual data for multilingual MT systems to increase the amount of synthetic parallel corpora while dealing with the data sparsity problem. We have shown significant improvements of up to +1.62 and +2.54 BLEU points over the bilingual baseline systems for both language pairs and released our datasets for the research community.


Overcoming the Rare Word Problem for low-resource language pairs in Neural Machine Translation
Thi-Vinh Ngo | Thanh-Le Ha | Phuong-Thai Nguyen | Le-Minh Nguyen
Proceedings of the 6th Workshop on Asian Translation

Among the six challenges of neural machine translation (NMT) coined by (Koehn and Knowles, 2017), rare-word problem is considered the most severe one, especially in translation of low-resource languages. In this paper, we propose three solutions to address the rare words in neural machine translation systems. First, we enhance source context to predict the target words by connecting directly the source embeddings to the output of the attention component in NMT. Second, we propose an algorithm to learn morphology of unknown words for English in supervised way in order to minimize the adverse effect of rare-word problem. Finally, we exploit synonymous relation from the WordNet to overcome out-of-vocabulary (OOV) problem of NMT. We evaluate our approaches on two low-resource language pairs: English-Vietnamese and Japanese-Vietnamese. In our experiments, we have achieved significant improvements of up to roughly +1.0 BLEU points in both language pairs.

How Transformer Revitalizes Character-based Neural Machine Translation: An Investigation on Japanese-Vietnamese Translation Systems
Thi-Vinh Ngo | Thanh-Le Ha | Phuong-Thai Nguyen | Le-Minh Nguyen
Proceedings of the 16th International Conference on Spoken Language Translation

While translating between East Asian languages, many works have discovered clear advantages of using characters as the translation unit. Unfortunately, traditional recurrent neural machine translation systems hinder the practical usage of those character-based systems due to their architectural limitations. They are unfavorable in handling extremely long sequences as well as highly restricted in parallelizing the computations. In this paper, we demonstrate that the new transformer architecture can perform character-based trans- lation better than the recurrent one. We conduct experiments on a low-resource language pair: Japanese-Vietnamese. Our models considerably outperform the state-of-the-art systems which employ word-based recurrent architectures.


Dealing with Out-Of-Vocabulary Problem in Sentence Alignment Using Word Similarity
Hai-Long Trieu | Le-Minh Nguyen | Phuong-Thai Nguyen
Proceedings of the 30th Pacific Asia Conference on Language, Information and Computation: Oral Papers


The JAIST-UET-MITI machine translation systems for IWSLT 2015
Hai-Long Trieu | Thanh-Quyen Dang | Phuong-Thai Nguyen | Le-Minh Nuyen
Proceedings of the 12th International Workshop on Spoken Language Translation: Evaluation Campaign


Bootstrapping Phrase-based Statistical Machine Translation via WSD Integration
Hien Vu Huy | Phuong-Thai Nguyen | Tung-Lam Nguyen | M.L Nguyen
Proceedings of the Sixth International Joint Conference on Natural Language Processing


Automated Extraction of Tree Adjoining Grammars from a Treebank for Vietnamese
Phuong Le-Hong | Thi Minh Huyen Nguyen | Phuong Thai Nguyen | Azim Roussanaly
Proceedings of the 10th International Workshop on Tree Adjoining Grammar and Related Frameworks (TAG+10)


Building a Large Syntactically-Annotated Corpus of Vietnamese
Phuong-Thai Nguyen | Xuan-Luong Vu | Thi-Minh-Huyen Nguyen | Van-Hiep Nguyen | Hong-Phuong Le
Proceedings of the Third Linguistic Annotation Workshop (LAW III)

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An Empirical Study of Vietnamese Noun Phrase Chunking with Discriminative Sequence Models
Le Minh Nguyen | Huong Thao Nguyen | Phuong Thai Nguyen | Tu Bao Ho | Akira Shimazu
Proceedings of the 7th Workshop on Asian Language Resources (ALR7)


A Multilingual Dependency Analysis System Using Online Passive-Aggressive Learning
Le-Minh Nguyen | Akira Shimazu | Phuong-Thai Nguyen | Xuan-Hieu Phan
Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL)