Papers by Dat Quoc Nguyen
A Capsule Network-based Embedding Model for Knowledge Graph Completion and Search Personalization (N19-1)
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| Challenge: | Existing knowledge graphs with billions of triples are incomplete, i.e., missing a lot of valid triples. |
| Approach: | They propose to embed relationship triples into a capsule network using a convolution layer and multiple filters to generate feature maps. |
| Outcome: | The proposed model outperforms strong search personalization baselines on two benchmark datasets and outperformed previous state-of-the-art models on WN18RR and FB15k-237. |
Improving Vietnamese-English Medical Machine Translation (2024.lrec-main)
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| Challenge: | Existing high-quality Vietnamese-English parallel datasets are inadequate for translation training. |
| Approach: | They introduce a high-quality Vietnamese-English parallel dataset for medical translation . they compare Google Translate, ChatGPT, and pre-trained bilingual/multilingual models . |
| Outcome: | The proposed dataset is compared with translation models from Google Translate and ChatGPT. |
RecGPT: Generative Pre-training for Text-based Recommendation (2024.acl-short)
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| Challenge: | Existing models for text-based recommendation lack data sparsity and flexibility to capture fluctuations in user preferences over time. |
| Approach: | They present the first domain-adapted and fully-trained large language model for text-based recommendation. |
| Outcome: | The proposed model outperforms baseline models on rating prediction and sequential recommendation tasks. |
PhoMT: A High-Quality and Large-Scale Benchmark Dataset for Vietnamese-English Machine Translation (2021.emnlp-main)
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| Challenge: | We present a high-quality and large-scale Vietnamese-English parallel dataset . our dataset is 2.9M pairs larger than the benchmark Vietnamese- English corpus . |
| Approach: | They present a large-scale Vietnamese-English parallel dataset with 3.02M sentence pairs . they compare strong neural baselines and well-known automatic translation engines . |
| Outcome: | The proposed dataset is 2.9M pairs larger than the benchmark Vietnamese-English corpus IWSLT15. |
Vietnamese Automatic Speech Recognition: A Revisit (2026.findings-eacl)
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| Challenge: | Existing datasets with low quality and inconsistent annotations are insufficient for high-quality models. |
| Approach: | They propose a pipeline for aggregating and preprocessing high-quality ASR datasets from diverse, potentially noisy, open-source sources. |
| Outcome: | The proposed pipeline provides a foundation for training and evaluating state-of-the-art Vietnamese ASR systems. |
BERTweet: A pre-trained language model for English Tweets (2020.emnlp-demos)
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| Challenge: | Experiments show that BERTweet outperforms strong baselines RoBERTa-base and XLM-R-base on three Tweet NLP tasks: Part-of-speech tagging, Named-entity recognition and text classification. |
| Approach: | They propose to train a pre-trained language model for English Tweets using the RoBERTa pre training procedure and use it to train the model. |
| Outcome: | Experiments show that the model outperforms baseline models on three Tweet NLP tasks: Part-of-speech tagging, Named-entity recognition and text classification. |
A Novel Embedding Model for Knowledge Base Completion Based on Convolutional Neural Network (N18-2)
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| Challenge: | Existing knowledge base embedding models are incomplete, i.e., missing a lot of valid triples. |
| Approach: | They propose a convolutional neural network embedding model for knowledge base completion that captures global relationships and transitional characteristics. |
| Outcome: | The proposed model outperforms state-of-the-art models on two benchmark datasets. |
PhoNLP: A joint multi-task learning model for Vietnamese part-of-speech tagging, named entity recognition and dependency parsing (2021.naacl-demos)
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| Challenge: | PhoNLP is a multi-task learning model for joint Vietnamese part-of-speech (POS) tagging, named entity recognition (NER) and dependency parsing. |
| Approach: | They propose a multi-task learning model for Vietnamese part-of-speech tagging, named entity recognition and dependency parsing that fine-tunes the pre-trained Vietnamese language model PhoBERT for each task independently. |
| Outcome: | The proposed model outperforms a single-task learning approach that fine-tunes the pre-trained Vietnamese language model PhoBERT for each task independently. |
Zero-Shot Text-to-Speech for Vietnamese (2025.acl-short)
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| Challenge: | Text-to-speech (TTS) synthesis has seen significant advancements in recent years. |
| Approach: | They propose to use PhoAudiobook to curated 941 hours of high-quality audio for Vietnamese text-to-speech models. |
| Outcome: | The proposed model improves on VALL-E, VoiceCraft, and XTTS-V2 models, highlighting their robustness in handling diverse linguistic contexts. |
VnCoreNLP: A Vietnamese Natural Language Processing Toolkit (N18-5)
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| Challenge: | Using word segmenters and POS taggers, Vietnamese NLP pipelines are no longer considered SOTA models for Vietnamese. |
| Approach: | They propose a Java NLP annotation pipeline for Vietnamese that provides rich linguistic annotations. |
| Outcome: | The proposed toolkit provides rich linguistic annotations to facilitate research work on Vietnamese NLP. |
COVID-19 Named Entity Recognition for Vietnamese (2021.naacl-main)
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| Challenge: | a new dataset is being developed to help fight the COVID-19 pandemic . the dataset is annotated for the named entity recognition task with newly-defined entity types . |
| Approach: | They present the first manually-annotated COVID-19 domain-specific dataset for Vietnamese . their dataset is annotated for the named entity recognition task with newly-defined entity types . |
| Outcome: | The proposed dataset is the first manually-annotated COVID-19 domain-specific dataset for Vietnamese. |
A Fast and Accurate Vietnamese Word Segmenter (L18-1)
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| Challenge: | Experimental results show that our approach outperforms previous state-of-the-art approaches in terms of accuracy and performance speed. |
| Approach: | They propose a method where rules are stored in an exception structure and new rules are only added to correct segmentation errors. |
| Outcome: | The proposed approach outperforms existing methods on Vietnamese treebank benchmarks. |
Joint Multilingual Knowledge Graph Completion and Alignment (2022.findings-emnlp)
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Vinh Tong, Dat Quoc Nguyen, Trung Thanh Huynh, Tam Thanh Nguyen, Quoc Viet Hung Nguyen, Mathias Niepert
| Challenge: | Existing work on multilingual KG completion has focused on entity and relation alignments, but understanding of how it can aid multilingual alignments is limited. |
| Approach: | They propose to combine two components that jointly accomplish KG completion and alignment. |
| Outcome: | The proposed model outperforms existing competitive baselines on a public multilingual benchmark and achieves state-of-the-art results. |
A Pilot Study of Text-to-SQL Semantic Parsing for Vietnamese (2020.findings-emnlp)
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| Challenge: | Semantic parsing is an important NLP task, but Vietnamese is a low-resource language. |
| Approach: | They extend EditSQL and IRNet semantic parsing baselines on Vietnamese datasets . they find automatic Vietnamese word segmentation improves parser results . |
| Outcome: | The proposed dataset improves on two strong parsing baselines for Vietnamese . the monolingual language model PhoBERT improves over the best multilingual language models. |
ClozeMath: Improving Mathematical Reasoning in Language Models by Learning to Fill Equations (2025.findings-acl)
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| Challenge: | Existing methods to train large language models do not capture how humans learn to think. |
| Approach: | They propose a method to fine-tune large language models for mathematical reasoning by using a text-infilling task that predicts masked equations from a given solution. |
| Outcome: | Experiments on GSM8K, MATH, and GSM-Symbolic show that ClozeMath surpasses baseline Masked Thought in performance and robustness with two test-time scaling decoding algorithms, Beam Search and Chain-of-Thought decoding. |
Who’s Who: Large Language Models Meet Knowledge Conflicts in Practice (2024.findings-emnlp)
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| Challenge: | Recent large-scale pretrained language models excel in tasks requiring natural language understanding, but they often "hallucinate" plausible but incorrect content due to outdated or incorrect pretraining information. |
| Approach: | They propose a public benchmark dataset to examine model’s behavior in knowledge conflict situations. |
| Outcome: | The proposed model induces conflicts by asking about a common property among entities having the same name, resulting in questions with up to 8 distinctive answers. |
MISCA: A Joint Model for Multiple Intent Detection and Slot Filling with Intent-Slot Co-Attention (2023.findings-emnlp)
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| Challenge: | Existing models for detecting multiple intents and filling slots are based on graphs but face problems . a joint model can exploit the correlations between intents, slots and slot filling tasks . |
| Approach: | They propose a joint model that captures correlations between intents and slot labels . they propose MISCA to incorporate an intent-slot co-attention mechanism and a label attention mechanism . |
| Outcome: | The proposed model outperforms previous models on two benchmark datasets. |
PhoBERT: Pre-trained language models for Vietnamese (2020.findings-emnlp)
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| Challenge: | Experimental results show that PhoBERT outperforms the recent best pre-trained multilingual model XLM-R in multiple Vietnamese-specific NLP tasks. |
| Approach: | They present PhoBERT with two versions, Phobert-base and PhoBRET-large, which are pre-trained for Vietnamese. |
| Outcome: | The proposed model outperforms the best pre-trained model XLM-R and improves the state-of-the-art in multiple Vietnamese-specific NLP tasks including Part-of speech tagging, Dependency parsing, Named-entity recognition and Natural language inference. |