Natural language processing (NLP) is a subfield of linguistics, artificial intelligence aiming at helping computers can understand human language and can interact with human.  With the rapid development of data science, NLP has a big progress in creating applications that can bring many benefits to life. Some applications of NLP are machine translation, chatbot, social media monitoring, survey analysis, targeted advertising, hiring and recruitment, voice assistants, spelling correction.

Our research group focuses on exploiting machine learning and deep learning techniques, incorporating with NLP features and other knowledges to develop high performance NLP applications. We also investigate methods to construct knowledge base and taxonomy for specific NLP tasks, and to create large datasets for training NLP tasks. See the slides here for more detail.

Contact: Assoc. Prof. Le Thanh Huong, Email:

Research Directions

Exploiting machine learning, deep learning techniques, in companied with NLP features to research and develop NLP applications in the following directions:

  • Information extraction: Several tasks are investigated including named entity recognition, relation extraction, event extraction.
  • Chatbot/question answering: Generation answers for questions based on different sources such as paragraphs, knowledge bases, databases, … Chatbot/question answering is used in many real-life applications such as customer service, study counseling, … We address different problems in this research direction including intent classification, slot tagging, question similarity, dialog management, …
  • Speech Technologies: Focusing on expressive speech synthesis, speech synthesis with state-of-the-art research, automatic speech recognition; speaker verification, speaker identification
  • Text Summarization: Summarizing single or multi-documents, either by picking up important sentences or creating new summaries with condensed content. We also look at query-based summarization, in which the answer is generated by summarizing all the documents returned by the query.
  • Sentiment analysis: Detecting positive/negative sentiment in text. Sentiment analysis is often used by businesses to detect sentiment in social data, and to understand customers.
  • Machine translation: We concentrate on several aspects: developing multilingual neural machine translation; increasing the performances (accuracy, speed) of the system; dealing with low resource languages; automatically building MT corpus for training machine translation systems.
  • Plagiarism detection: Automatically identifying the copied fragments in a suspicious document from other source documents. We also concern about cross-language plagiarism detection where the source of plagiarism is in a different language.
  • Vietnamese spelling correction: Spelling and grammatical errors make input texts difficult to understand. If such documents are used for training, it leads to bad model quality. In real-world NLP problems, we often meet texts with a lot of typos. Because of that, data should be cleaned before using. We focus on correcting spelling errors in two data types: academic text and social data.

Research Problems

  • Synonym discovery from multiple sources: The project aims at discovering synonyms from multiple Web data sources. Synonyms are in form of various alias of the same entity, or equivalent representations of attribute relationships. The main sources come from user interaction with web search engines such as web search logs, semi-structured data such as web tables, and unstructured data such as web documents.
  • Weakly supervised aspect extraction: The project aims at extracting domain aspects from user-generated content which serves as an essential step in opinion mining. It tackles the bottleneck of data annotation by studying the paradigm of weak supervision empowered by neural representation and neural learning frameworks.
  • Weakly supervised taxonomy construction: A taxonomy is a scheme of classification that helps to organize and index knowledge. Generally, the development and the maintenance of a taxonomy is a labor-intensive task requiring significant resources and expertise. Our objective aims at exploring weak supervision to accelerate the process in an automated manner while keeping a minimum requirement on manual tasks.
  • Knowledge base construction from semi-structured documents: Today, our data universe is increasing exponentially and more than 70% of those data are unstructured and semi-structures (e.g. word, pdf, excel files). Those data are commonly un-touched as they are not in the right forms for data analytic software. Our objective is to develop natural language understanding methods to extract valuable information in semi-structured documents. We are then able to construct knowledge bases, which benefit further analytics and beyond.

Team Members

Assoc. Prof. Le Thanh Huong
Team Leader

Assoc. Prof. Nguyen Thi Kim Anh

Dr. Nguyen Thi Thu Trang

Dr. Nguyen Kiem Hieu

Dr. Tran Viet Trung

Post-doc and PhD Students

Ha Thi Thanh
PhD Student

Luu Minh Tuan
PhD Student

Projects and Solutions

Tools and Resources

Latest Publications

Publications in 2022

  1. Thi-Thanh Ha, Van-Nha Nguyen, Kiem-Hieu Nguyen, Kim-Anh Nguyen, Quang-Khoat Than. Utilizing SBERT For Finding Similar Questions in Community Question Answering. 13th International Conference on Knowledge and Systems Engineering (KSE). 1-6. Bangkok, Thailand. 10/11/2021

Publications in 2021

  1. Ha Nguyen Tien, Dat Nguyen Huu, Huong Le Thanh, Vinh Nguyen Van and Minh Nguyen Quang. KC4Align: Improving Sentence Alignment Method for Low-resource Language Pairs. The 35th Pacific Asia Conference on Language, Information and Computation (PACLIC). 358-367. 05/11/2021
  2. Huong T. Le, Que X. Bui. Keyphrase Extraction Using PageRank and Word Features. RIVF (Research, Innovation and Vision for the Future). 257-261. 02/12/2021
  3. Nguyen Van Son, Le Thanh Huong, Nguyen Chi Thanh. A two-phase plagiarism detection system based on multi-layer LSTM Networks. IAES International Journal of Artificial Intelligence. 636-648. 26/02/2021
  4. Minh-Tuan Luu, Thanh-Huong Le, Minh-Tan Hoang. An Effective Deep Learning Approach for Extractive Text Summarization. IJCSE Indian Journal of Computer Science and Engineering. 434-444. 07/04/2021
  5. Huong T. Le, Dung T. Cao, Trung H. Bui, Long T. Luong and Huy Q. Nguyen. Improve Quora Question Pair Dataset for Question Similarity Task. RIVF (Research, Innovation and Vision for the Future). 279-283. 02/12/2021
  6. Tuan Minh Luu, Huong Thanh Le, Tan Minh Hoang. A HYBRID MODEL USING THE PRETRAINED BERT AND DEEP NEURAL NETWORKS WITH RICH FEATURE FOR EXTRACTIVE TEXT SUMMARIZATION. Journal of Computer Science and Cybernetics. 123--143. 13/05/2021
  7. Nguyen Thi Thu Trang, Nguyen Hoang Ky, Albert Rilliard, Christophe D’Alessandro. Prosodic Boundary Prediction Model for Vietnamese Text-To-Speech. The 22th Conference of the International Speech Communication Association (Interspeech 2021). 3885-3889. Brno, Czech Republic. 30/08/2021
  8. Dang Trung Duc Anh, Nguyen Thi Thu Trang. TDP – A Hybrid Diacritic Restoration with Transformer Decoder. The 34th Pacific Asia Conference on Language, Information and Computation (PACLIC 2020). 76-83. Hanoi, Vietnam. 24/10/2020
  9. Bùi Thị Mai Anh, Nguyễn Thị Thu Trang. A Feature-Augmented Deep Learning Model for Extractive Summarization. INISCOM 2021. Vol 379. Le Quy Don University, Hanoi, Vietnam. 22/04/2021
  10. Thi-Trang Nguyen, Huu-Hoang Nguyen, Kiem-Hieu Nguyen. A Study on Seq2seq for Sentence Compression in Vietnamese. Proceedings of the 34th Pacific Asia Conference on Language, Information and Computation. 488-495. Hanoi, Vietnam. 24/10/2020
  11. Thi-Thanh Ha, Van-Nha Nguyen, Kiem-Hieu Nguyen, Kim Anh Nguyen, Tien-Thanh Nguyen. Utilizing Bert for Question Retrieval on Vietnamese E-commerce Sites. Proceedings of the 34th Pacific Asia Conference on Language, Information and Computation. 92-99. Hanoi, Vietnam. 24/10/2020
  12. Thi-Nhung Nguyen, Kiem-Hieu Nguyen, Young-In Song, Tuan-Dung Cao. An Uncertainty-Aware Encoder for Aspect Detection. Findings of the Association for Computational Linguistics: EMNLP 2021. 797–806. Punta Cana, Dominican Republic. 07/11/2021

Publications in 2020

  1. Viet Nguyen Quoc, Huong Le Thanh, Tuan Luu Minh. Abstractive Text Summarization Using LSTMs with Rich Features. 16th International Conference of the Pacific Association for Computational Linguistics. 28-40. Hanoi. 11/10/2019
  2. Nguyen Thi Thu Trang, Nguyen Hoang Ky, Hoang Son, Nguyen Thanh Hung, Nguyen Danh Huan. Natural Language Understanding in SmartDialog - a Platform for Vietnamese Intelligent Interactions. International Conference on Natural Language Processing and Information Retrieval NLPIR 2019. 01/06/2019
  3. Bui Thi Mai Anh, Nguyen Tra My, Nguyen Thi Thu Trang. Enhanced Genetic Algorithm for Single Document Extractive Summarization. SOICT 2019. 04/12/2019
  4. Nguyen Thi Thu Trang, Dang Xuan Bach, Nguyen Xuan Tung. A Hybrid Method for Vietnamese Text Normalization. International Conference on Natural Language Processing and Information Retrieval NLPIR 2019. 01/06/2019
  5. Van-Hai Vu, Quang-Phuoc Nguyen, Kiem-Hieu Nguyen, Joon-Choul Shin, Cheol-Young Ock. Korean-Vietnamese Neural Machine Translation with Named Entity Recognition and Part-of-Speech Tags. IEICE Transactions on Information and Systems. 866-873. 01/04/2020
  6. Thanh Thi Ha, Atsuhiro Takasu, Thanh Chinh Nguyen, Kiem Hieu Nguyen, Van Nha Nguyen, Kim Anh Nguyen, Son Giang Tran. Supervised attention for answer selection in community question answering. IAES International Journal of Artificial Intelligence. 203. 01/06/2020