Dinh Dien

Also published as: Dien Dinh


Multi-level Community-awareness Graph Neural Networks for Neural Machine Translation
Binh Nguyen | Long Nguyen | Dien Dinh
Proceedings of the 29th International Conference on Computational Linguistics

Neural Machine Translation (NMT) aims to translate the source- to the target-language while preserving the original meaning. Linguistic information such as morphology, syntactic, and semantics shall be grasped in token embeddings to produce a high-quality translation. Recent works have leveraged the powerful Graph Neural Networks (GNNs) to encode such language knowledge into token embeddings. Specifically, they use a trained parser to construct semantic graphs given sentences and then apply GNNs. However, most semantic graphs are tree-shaped and too sparse for GNNs which cause the over-smoothing problem. To alleviate this problem, we propose a novel Multi-level Community-awareness Graph Neural Network (MC-GNN) layer to jointly model local and global relationships between words and their linguistic roles in multiple communities. Intuitively, the MC-GNN layer substitutes a self-attention layer at the encoder side of a transformer-based machine translation model. Extensive experiments on four language-pair datasets with common evaluation metrics show the remarkable improvements of our method while reducing the time complexity in very long sentences.


Identifying Authors Based on Stylometric measures of Vietnamese texts
Ho Ngoc Lam | Vo Diep Nhu | Dinh Dien | Nguyen Tuyet Nhung
Proceedings of the 34th Pacific Asia Conference on Language, Information and Computation


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A Novel Approach for Handling Unknown Word Problem in Chinese-Vietnamese Machine Translation
Phuoc Tran | Dien Dinh
International Journal of Computational Linguistics & Chinese Language Processing, Volume 19, Number 1, March 2014

Building English-Vietnamese Named Entity Corpus with Aligned Bilingual News Articles
Quoc Hung Ngo | Dinh Dien | Werner Winiwarter
Proceedings of the Fifth Workshop on South and Southeast Asian Natural Language Processing


An ontology-driven system for detecting global health events
Nigel Collier | Reiko Matsuda Goodwin | John McCrae | Son Doan | Ai Kawazoe | Mike Conway | Asanee Kawtrakul | Koichi Takeuchi | Dinh Dien
Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010)


POS-Tagger for English-Vietnamese Bilingual Corpus
Dinh Dien | Hoang Kiem
Proceedings of the HLT-NAACL 2003 Workshop on Building and Using Parallel Texts: Data Driven Machine Translation and Beyond

A hybrid approach to word order transfer in the English-to-Vietnamese machine translation
Dinh Dien | Nguyen Luu Thuy Ngan | Do Xuan Quang | Van Chi Nam
Proceedings of Machine Translation Summit IX: Papers

Word Order transfer is a compulsory stage and has a great effect on the translation result of a transfer-based machine translation system. To solve this problem, we can use fixed rules (rule-based) or stochastic methods (corpus-based) which extract word order transfer rules between two languages. However, each approach has its own advantages and disadvantages. In this paper, we present a hybrid approach based on fixed rules and Transformation-Based Learning (or TBL) method. Our purpose is to transfer automatically the English word orders into the Vietnamese ones. The learning process will be trained on the annotated bilingual corpus (named EVC: English-Vietnamese Corpus) that has been automatically word-aligned, phrase-aligned and POS-tagged. This transfer result is being used for the transfer module in the English-Vietnamese transfer-based machine translation system.

BTL: a hybrid model for English-Vietnamese machine translation
Dinh Dien | Kiem Hoang | Eduard Hovy
Proceedings of Machine Translation Summit IX: Papers

Machine Translation (MT) is the most interesting and difficult task which has been posed since the beginning of computer history. The highest difficulty which computers had to face with, is the built-in ambiguity of Natural Languages. Formerly, a lot of human-devised rules have been used to disambiguate those ambiguities. Building such a complete rule-set is time-consuming and labor-intensive task whilst it doesn’t cover all the cases. Besides, when the scale of system increases, it is very difficult to control that rule-set. In this paper, we present a new model of learning-based MT (entitled BTL: Bitext-Transfer Learning) that learns from bilingual corpus to extract disambiguating rules. This model has been experimented in English-to-Vietnamese MT system (EVT) and it gave encouraging results.


Building a Training Corpus for Word Sense Disambiguation in English-to-Vietnamese Machine Translation
Dien Dinh
COLING-02: Machine Translation in Asia


An Approach to Parsing Vietnamese Noun Compounds
Dinh Dien | Hoang Kiem
Proceedings of the Seventh International Workshop on Parsing Technologies