Thanh Vu


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Automatic Post-Editing for Vietnamese
Thanh Vu | Dai Quoc Nguyen
Proceedings of the 19th Annual Workshop of the Australasian Language Technology Association

Automatic post-editing (APE) is an important remedy for reducing errors of raw translated texts that are produced by machine translation (MT) systems or software-aided translation. In this paper, we present a systematic approach to tackle the APE task for Vietnamese. Specifically, we construct the first large-scale dataset of 5M Vietnamese translated and corrected sentence pairs. We then apply strong neural MT models to handle the APE task, using our constructed dataset. Experimental results from both automatic and human evaluations show the effectiveness of the neural MT models in handling the Vietnamese APE task.


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BERTweet: A pre-trained language model for English Tweets
Dat Quoc Nguyen | Thanh Vu | Anh Tuan Nguyen
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

We present BERTweet, the first public large-scale pre-trained language model for English Tweets. Our BERTweet, having the same architecture as BERT-base (Devlin et al., 2019), is trained using the RoBERTa pre-training procedure (Liu et al., 2019). Experiments show that BERTweet outperforms strong baselines RoBERTa-base and XLM-R-base (Conneau et al., 2020), producing better performance results than the previous state-of-the-art models on three Tweet NLP tasks: Part-of-speech tagging, Named-entity recognition and text classification. We release BERTweet under the MIT License to facilitate future research and applications on Tweet data. Our BERTweet is available at

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WNUT-2020 Task 2: Identification of Informative COVID-19 English Tweets
Dat Quoc Nguyen | Thanh Vu | Afshin Rahimi | Mai Hoang Dao | Linh The Nguyen | Long Doan
Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)

In this paper, we provide an overview of the WNUT-2020 shared task on the identification of informative COVID-19 English Tweets. We describe how we construct a corpus of 10K Tweets and organize the development and evaluation phases for this task. In addition, we also present a brief summary of results obtained from the final system evaluation submissions of 55 teams, finding that (i) many systems obtain very high performance, up to 0.91 F1 score, (ii) the majority of the submissions achieve substantially higher results than the baseline fastText (Joulin et al., 2017), and (iii) fine-tuning pre-trained language models on relevant language data followed by supervised training performs well in this task.


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ETNLP: A Visual-Aided Systematic Approach to Select Pre-Trained Embeddings for a Downstream Task
Son Vu Xuan | Thanh Vu | Son Tran | Lili Jiang
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)

Given many recent advanced embedding models, selecting pre-trained word representation (i.e., word embedding) models best fit for a specific downstream NLP task is non-trivial. In this paper, we propose a systematic approach to extracting, evaluating, and visualizing multiple sets of pre-trained word embed- dings to determine which embeddings should be used in a downstream task. First, for extraction, we provide a method to extract a subset of the embeddings to be used in the downstream NLP tasks. Second, for evaluation, we analyse the quality of pre-trained embeddings using an input word analogy list. Finally, we visualize the embedding space to explore the embedded words interactively. We demonstrate the effectiveness of the proposed approach on our pre-trained word embedding models in Vietnamese to select which models are suitable for a named entity recogni- tion (NER) task. Specifically, we create a large Vietnamese word analogy list to evaluate and select the pre-trained embedding models for the task. We then utilize the selected embed- dings for the NER task and achieve the new state-of-the-art results on the task benchmark dataset. We also apply the approach to another downstream task of privacy-guaranteed embedding selection, and show that it helps users quickly select the most suitable embeddings. In addition, we create an open-source system using the proposed systematic approach to facilitate similar studies on other NLP tasks. The source code and data are available at https: //

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A Capsule Network-based Embedding Model for Knowledge Graph Completion and Search Personalization
Dai Quoc Nguyen | Thanh Vu | Tu Dinh Nguyen | Dat Quoc Nguyen | Dinh Phung
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

In this paper, we introduce an embedding model, named CapsE, exploring a capsule network to model relationship triples (subject, relation, object). Our CapsE represents each triple as a 3-column matrix where each column vector represents the embedding of an element in the triple. This 3-column matrix is then fed to a convolution layer where multiple filters are operated to generate different feature maps. These feature maps are reconstructed into corresponding capsules which are then routed to another capsule to produce a continuous vector. The length of this vector is used to measure the plausibility score of the triple. Our proposed CapsE obtains better performance than previous state-of-the-art embedding models for knowledge graph completion on two benchmark datasets WN18RR and FB15k-237, and outperforms strong search personalization baselines on SEARCH17.

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Identifying Patients with Pain in Emergency Departments using Conventional Machine Learning and Deep Learning
Thanh Vu | Anthony Nguyen | Nathan Brown | James Hughes
Proceedings of the 17th Annual Workshop of the Australasian Language Technology Association

Pain is the main symptom that patients present with to the emergency department (ED). Pain management, however, is often poorly done aspect of emergency care and patients with painful conditions can endure long waits before their pain is assessed or treated. To improve pain management quality, identifying whether or not an ED patient presents with pain is an important task and allows for further investigation of the quality of care provided. In this paper, machine learning was utilised to handle the task of automatically detecting patients who present at EDs with pain from retrospective data. Experimental results on a manually annotated dataset show that our proposed machine learning models achieve high performances, in which the highest accuracy and macro-averaged F1 are 91.00% and 90.96%, respectively.


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VnCoreNLP: A Vietnamese Natural Language Processing Toolkit
Thanh Vu | Dat Quoc Nguyen | Dai Quoc Nguyen | Mark Dras | Mark Johnson
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations

We present an easy-to-use and fast toolkit, namely VnCoreNLP—a Java NLP annotation pipeline for Vietnamese. Our VnCoreNLP supports key natural language processing (NLP) tasks including word segmentation, part-of-speech (POS) tagging, named entity recognition (NER) and dependency parsing, and obtains state-of-the-art (SOTA) results for these tasks. We release VnCoreNLP to provide rich linguistic annotations to facilitate research work on Vietnamese NLP. Our VnCoreNLP is open-source and available at:

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A Fast and Accurate Vietnamese Word Segmenter
Dat Quoc Nguyen | Dai Quoc Nguyen | Thanh Vu | Mark Dras | Mark Johnson
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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NIHRIO at SemEval-2018 Task 3: A Simple and Accurate Neural Network Model for Irony Detection in Twitter
Thanh Vu | Dat Quoc Nguyen | Xuan-Son Vu | Dai Quoc Nguyen | Michael Catt | Michael Trenell
Proceedings of the 12th International Workshop on Semantic Evaluation

This paper describes our NIHRIO system for SemEval-2018 Task 3 “Irony detection in English tweets.” We propose to use a simple neural network architecture of Multilayer Perceptron with various types of input features including: lexical, syntactic, semantic and polarity features. Our system achieves very high performance in both subtasks of binary and multi-class irony detection in tweets. In particular, we rank at least fourth using the accuracy metric and sixth using the F1 metric. Our code is available at:


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From Word Segmentation to POS Tagging for Vietnamese
Dat Quoc Nguyen | Thanh Vu | Dai Quoc Nguyen | Mark Dras | Mark Johnson
Proceedings of the Australasian Language Technology Association Workshop 2017


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Sentiment Classification on Polarity Reviews: An Empirical Study Using Rating-based Features
Dai Quoc Nguyen | Dat Quoc Nguyen | Thanh Vu | Son Bao Pham
Proceedings of the 5th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis