VIETNAMESE

Revealing Weaknesses of Vietnamese Language Models Through Unanswerable Questions in Machine Reading Comprehension (2023.eacl-srw)

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.

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Son Quoc Tran, Phong Nguyen-Thuan Do, Kiet Van Nguyen, Ngan Luu-Thuy Nguyen

Reasoning

Challenge:	Existing problems in Vietnamese Machine Reading Comprehension systems are limited due to multilinguality, which limits the ability of multilingual models to develop state-of-the-art systems.
Approach:	They propose to modify the process of annotating unanswerable questions to improve the quality of unanswered questions to a higher level of difficulty for Machine Reading Comprehension systems to solve.
Outcome:	The proposed modification improves the quality of unanswerable questions to a higher level of difficulty for Machine Reading Comprehension systems to solve.

A Simple and Fast Strategy for Handling Rare Words in Neural Machine Translation (2022.aacl-srw)

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Nguyen-Hoang Minh-Cong, Vinh Thi Ngo, Van Vinh Nguyen

Abstractive Text Summarization Using the BRIO Training Paradigm (2023.findings-acl)

Challenge:	Neural Machine Translation (NMT) has been gaining popularity due to its ability to bias in highfrequency words, low-frequency words have little chance of being considered in the inference process.
Approach:	They propose a strategy for integrating constraints during the training and decoding process to improve the translation of rare words.
Outcome:	The proposed approach improves translation of rare words in high and low-resource translation tasks, showing improvements of up to +1.8 BLEU scores over baseline systems.

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Khang Lam, Thieu Doan, Khang Pham, Jugal Kalita

Summarization

Challenge:	Existing abstractive summarization models rely heavily on reference summaries and lack control over their performance.
Approach:	They propose a BRIO paradigm to reduce the dependence on reference summaries by fine-tuning pre-trained language models and training them with the paradigm.
Outcome:	The proposed paradigm outperforms existing models on Vietnamese and CNNDM datasets while maintaining the main content of the original text.

Improving Vietnamese-English Cross-Lingual Retrieval for Legal and General Domains (2025.naacl-short)

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Toan Ngoc Nguyen, Nam Le Hai, Nguyen Doan Hieu, Dai An Nguyen, Linh Ngo Van, Thien Huu Nguyen, Sang Dinh

Information Retrieval

Challenge:	Existing document retrieval systems focus on a single language, targeting resource-rich languages like English or Chinese.
Approach:	They propose auxiliary loss function and symmetrical training strategy for cross-lingual retrieval between Vietnamese and English . they propose a dataset that covers the general domain and extends to the legal field .
Outcome:	The proposed dataset significantly improves state-of-the-art models on cross-lingual retrieval tasks.

VnCoreNLP: A Vietnamese Natural Language Processing Toolkit (N18-5)

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Thanh Vu, Dat Quoc Nguyen, Dai Quoc Nguyen, Mark Dras, Mark Johnson

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.

VLUE: A New Benchmark and Multi-task Knowledge Transfer Learning for Vietnamese Natural Language Understanding (2024.findings-naacl)

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Phong Do, Son Tran, Phu Hoang, Kiet Nguyen, Ngan Nguyen

ViSoLex: An Open-Source Repository for Vietnamese Social Media Lexical Normalization (2025.coling-demos)

Challenge:	a lack of standard evaluation metrics and benchmarks makes it difficult to identify strengths of Vietnamese NLP models.
Approach:	They propose to establish a standardized set of benchmarks for Vietnamese NLU . they propose to evaluate Vietnamese language understanding models using a pre-trained model .
Outcome:	The proposed model combines proficiency of a multilingual pre-trained model with Vietnamese linguistic knowledge.

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Anh Thi-Hoang Nguyen, Dung Ha Nguyen, Kiet Van Nguyen

Challenge:	ViSoLex is an open-source repository for Vietnamese lexical normalization . it provides two core services: Non-Standard Word (NSW) Lookup and Lexical Normalization enabling users to retrieve standard forms of informal language and standardize text containing NSWs.
Approach:	They propose to integrate pre-trained language models and weakly supervised learning techniques to ensure accurate and efficient normalization.
Outcome:	The system provides two core services: Non-Standard Word (NSW) Lookup and Lexical Normalization, enabling users to retrieve standard forms of informal language and standardize text containing NSWs.

Counterfactual Data Augmentation for Neural Machine Translation (2021.naacl-main)

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Qi Liu, Matt Kusner, Phil Blunsom

ViT5: Pretrained Text-to-Text Transformer for Vietnamese Language Generation (2022.naacl-srw)

Challenge:	Neural machine translation models often rely on large-scale parallel corpora for training, exhibiting degraded performance on low-resource languages.
Approach:	They propose a method that interprets language models and phrasal alignment causally and generates augmented parallel translation corpora by sampling new source phrases from a masked language model.
Outcome:	The proposed method improves translation, backtranslation and translation robustness on IWSLT’15 English Vietnamese, WMT’17 English - German, and WMT'18 English – Turkish.

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Long Phan, Hieu Tran, Hieu Nguyen, Trieu H. Trinh

MERLIN: A Testbed for Multilingual Multimodal Entity Recognition and Linking (2026.tacl-1)

Challenge:	Existing models for the English language have been used to train on large corpus of high-quality texts.
Approach:	They present a pretrained Transformer-based encoder-decoder model for the Vietnamese language . they benchmark ViT5 on two downstream text generation tasks .
Outcome:	The proposed model outperforms existing models on Vietnamese Abstractive Summarization and Named Entity Recognition tasks.

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Sathyanarayanan Ramamoorthy, Vishwa Shah, Simran Khanuja, Zaid Sheikh, Shan Jie, Ann Chia, Shearman Chua, Graham Neubig

EduPulse: A Practical LLM-Enhanced Opinion Mining System for Vietnamese Student Feedback in Educational Platforms (2026.eacl-industry)

Challenge:	Existing methods for multilingual entity linking are limited by textual contexts and limited resources.
Approach:	They propose a testbed system for multilingual multimodal entity linking using BBC news articles paired with corresponding images in five languages.
Outcome:	The proposed system improves accuracy for entities with ambiguous textual contexts and models with weak multilingual abilities.

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Nguyen Xuan Phuc, Phi Nguyen Xuan, Vinh-Tiep Nguyen, Thìn Dang Van, Ngan Luu-Thuy Nguyen

Sentiment Analysis

Challenge:	EduPulse is a system designed specifically to analyze student feedback in Vietnamese.
Approach:	They propose a system that analyzes student feedback in Vietnamese to improve opinion mining.
Outcome:	The proposed system performs four opinion analysis tasks in Vietnamese . it is scalable and maintainable, and it is cost-effective, the authors show .

Logographic Information Aids Learning Better Representations for Natural Language Inference (2022.findings-aacl)

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Zijian Jin, Duygu Ataman

Parameter Space Factorization for Zero-Shot Learning across Tasks and Languages (2021.tacl-1)

Challenge:	Statistical language models often ignore information related to logographic features of written text . many languages, including Chinese and Vietnamese, use logographic writing systems .
Approach:	They propose to provide language models with logographic information to learn better semantic representations.
Outcome:	The proposed model improves semantic representations in six languages with logographic writing systems.

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Edoardo M. Ponti, Ivan Vulić, Ryan Cotterell, Marinela Parovic, Roi Reichart, Anna Korhonen

ViHealthBERT: Pre-trained Language Models for Vietnamese in Health Text Mining (2022.lrec-1)

Challenge:	Currently, there are only 24 languages in the world that have not been annotated . transferring knowledge across domains is a common solution .
Approach:	They propose a Bayesian generative model for the space of neural parameters that factorizes into latent variables for each language and each task.
Outcome:	The proposed model can perform better than state-of-the-art methods with a typologically diverse sample of 33 languages from 4 continents and 11 families.

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Nguyen Minh, Vu Hoang Tran, Vu Hoang, Huy Duc Ta, Trung Huu Bui, Steven Quoc Hung Truong

Sailor: Open Language Models for South-East Asia (2024.emnlp-demo)

Challenge:	Recent large-scale language models show remarkable achievements in key NLP tasks such as Question Answering and Text Summarization.
Approach:	They propose a domain-specific pre-trained Vietnamese language model that outperforms the general domain language models.
Outcome:	The proposed model outperforms the general domain language models in Vietnamese datasets while outperforming the general-domain language models.

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Longxu Dou, Qian Liu, Guangtao Zeng, Jia Guo, Jiahui Zhou, Xin Mao, Ziqi Jin, Wei Lu, Min Lin

Aurora-M: Open Source Continual Pre-training for Multilingual Language and Code (2025.coling-industry)

Challenge:	Large language models (LLMs) rely on English data for training, but are often not comparable across other languages.
Approach:	They propose to develop a family of open language models for SEA languages . they use BPE dropout, aggressive data cleaning and deduplication to improve model robustness .
Outcome:	The proposed models perform well across four benchmarks, including commonsense reasoning, question answering, reading comprehension and examination.

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Taishi Nakamura, Mayank Mishra, Simone Tedeschi, Yekun Chai, Jason T. Stillerman, Felix Friedrich, Prateek Yadav, Tanmay Laud, Vu Minh Chien, Terry Yue Zhuo, Diganta Misra, Ben Bogin, Xuan-Son Vu, Marzena Karpinska, Arnav Varma Dantuluri, Wojciech Kusa, Tommaso Furlanello, Rio Yokota, Niklas Muennighoff, Suhas Pai, Tosin Adewumi, Veronika Laippala, Xiaozhe Yao, Adalberto Barbosa Junior, Aleksandr Drozd, Jordan Clive, Kshitij Gupta, Liangyu Chen, Qi Sun, Ken Tsui, Nour Moustafa-Fahmy, Nicolo Monti, Tai Dang, Ziyang Luo, Tien-Tung Bui, Roberto Navigli, Virendra Mehta, Matthew Blumberg, Victor May, Hiep Nguyen, Sampo Pyysalo

A Large-Scale Benchmark for Vietnamese Sentence Paraphrases (2025.findings-naacl)

Challenge:	Pretrained language models are integral part of AI applications, but their high computational cost limits accessibility.
Approach:	They evaluate Aurora-M, a 15B parameter multilingual open-source model trained on English, Finnish, Hindi, Japanese, Vietnamese, and code.
Outcome:	The proposed model outperforms existing models on English, Finnish, Hindi, Japanese, Vietnamese, and code.

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Sang Quang Nguyen, Kiet Van Nguyen

Text Generation

Challenge:	1.2M original–paraphrase pairs were generated using a hybrid approach to generate high-quality paraphrases.
Approach:	They present a high-quality Vietnamese dataset for sentence paraphrasing . they used automatic paraphrase generation and manual evaluation to ensure high quality .
Outcome:	The proposed dataset is the first large-scale study on Vietnamese paraphrasing . it combines automatic paraphrase generation with manual evaluation to ensure high quality .

Medical Spoken Named Entity Recognition (2025.naacl-industry)

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Khai Le-Duc, David Thulke, Hung-Phong Tran, Long Vo-Dang, Khai-Nguyen Nguyen, Truong-Son Hy, Ralf Schlüter

ViGPTQA - State-of-the-Art LLMs for Vietnamese Question Answering: System Overview, Core Models Training, and Evaluations (2023.emnlp-industry)

Challenge:	Named Entity Recognition (NER) aims to extract named entities from speech and categorise them into types like person, location, organization, etc.
Approach:	They present a spoken NER dataset in the medical domain using pre-trained models that are encoder-only and sequence-to-sequence.
Outcome:	The dataset is the largest spoken NER dataset in the world regarding the number of entity types, featuring 18 distinct types.

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Minh Thuan Nguyen, Khanh Tung Tran, Nhu Van Nguyen, Xuan-Son Vu

Question Answering

Challenge:	Large language models (LLMs) and their applications in low-resource languages are limited due to lack of training data and benchmarking datasets.
Approach:	They propose a question-response system for Vietnamese that uses LLMs . they propose to open-source the model and train it on benchmark datasets based on Vietnamese data .
Outcome:	The proposed question answering system for Vietnamese is open-source and performant . it can learn and capture human-like text, but there is a gap in evaluations for Vietnamese .

MultiMed: Multilingual Medical Speech Recognition via Attention Encoder Decoder (2025.acl-industry)

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Khai Le-Duc, Phuc Phan, Tan-Hanh Pham, Bach Phan Tat, Minh-Huong Ngo, Thanh Nguyen-Tang, Truong-Son Hy

VlogQA: Task, Dataset, and Baseline Models for Vietnamese Spoken-Based Machine Reading Comprehension (2024.eacl-long)

Challenge:	Multilingual automatic speech recognition (ASR) in the medical domain is a critical foundational task, serving a wide range of downstream applications such as speech translation, spoken language understanding, and voice-activated assistants.
Approach:	They present the first multilingual medical ASR dataset and the first collection of small-to-large end-to end medical APR models spanning five languages: Vietnamese, English, German, French, and Mandarin Chinese.
Outcome:	The proposed model covers Vietnamese, English, German, French, and Mandarin Chinese, and is the first multilingual ASR dataset across five languages.

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Thinh Ngo, Khoa Dang, Son Luu, Kiet Nguyen, Ngan Nguyen

Machine Reading Comprehension

Challenge:	Existing datasets for machine reading comprehension tasks in Vietnamese focus on written documents, such as Wikipedia articles, online newspapers, or textbooks.
Approach:	They propose to capture Vietnamese spoken language in natural settings and use it to create a machine-learning corpus for machine reading comprehension tasks.
Outcome:	The proposed corpus consists of 10,076 question-answer pairs based on 1,230 transcript documents sourced from YouTube .

ViDeBERTa: A powerful pre-trained language model for Vietnamese (2023.findings-eacl)

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Cong Dao Tran, Nhut Huy Pham, Anh Tuan Nguyen, Truong Son Hy, Tu Vu

Zero-Shot Text-to-Speech for Vietnamese (2025.acl-short)

Challenge:	Existing models for Vietnamese that perform well on downstream tasks, such as Question answering, are based on Transformer.
Approach:	They propose a pre-trained monolingual Vietnamese model with three versions . they fine-tune and evaluate the model on three important natural language downstream tasks, Part-of-speech tagging, Named-entity recognition, and Question answering.
Outcome:	The proposed model outperforms the existing model on three important natural language downstream tasks, Part-of-speech tagging, Named-entity recognition, and Question answering.

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Thi Vu, Linh The Nguyen, Dat Quoc Nguyen

Sentiment Reasoning for Healthcare (2025.acl-industry)

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.

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Khai-Nguyen Nguyen, Khai Le-Duc, Bach Phan Tat, Le Duy, Long Vo-Dang, Truong-Son Hy

ViLexNorm: A Lexical Normalization Corpus for Vietnamese Social Media Text (2024.eacl-long)

Challenge:	Sentiment Reasoning is an auxiliary task in sentiment analysis where the model predicts both the sentiment label and generates the rationale behind it based on the input transcript.
Approach:	They propose a task - Sentiment Reasoning - for both speech and textmodalities and propose 'multimodal multitask framework' . they propose to use a model that generates the rationale behind each predicted label and provides a rationale for model prediction with quality semantically comparable to humans.
Outcome:	The proposed task improves model transparency by providing rationale for model prediction with quality semantically comparable to humans while improving model’s classification performance.

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Thanh-Nhi Nguyen, Thanh-Phong Le, Kiet Nguyen

Challenge:	Lexical normalization is a fundamental task in Natural Language Processing (NLP) it involves the transformation of words into their canonical forms.
Approach:	They present a corpus of Vietnamese words annotated by human annotators for the Vietnamese lexical normalization task.
Outcome:	The best-performing system achieved 57.74% using the Error Reduction Rate (ERR) metric with the Leave-As-Is (LAI) baseline.

VN-MTEB: Vietnamese Massive Text Embedding Benchmark (2026.findings-eacl)

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Loc Pham, Tung Luu, Thu Vo, Minh Nguyen, Viet Hoang

Representation Learning

Challenge:	a lack of large-scale test datasets makes it difficult to evaluate AI models before deploying them in real-world projects.
Approach:	They propose a Vietnamese benchmark for embedding models that leverages large language models and embeddable models to translate and filter samples from the Massive Multilingual Text Embedding Benchmark.
Outcome:	The proposed benchmark outperforms existing models in Vietnamese and English tasks with 41 datasets.

Answering Legal Questions by Learning Neural Attentive Text Representation (2020.coling-main)

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Phi Manh Kien, Ha-Thanh Nguyen, Ngo Xuan Bach, Vu Tran, Minh Le Nguyen, Tu Minh Phuong

Representation Learning

Challenge:	Existing methods for retrieval-based question answering are limited by legal documents and long and complicated documents.
Approach:	They propose a retrieval-based model for answering legal questions at the article level by learning neural attentive text representation.
Outcome:	The proposed model outperforms state-of-the-art retrieval-based methods on an annotated corpus of 5,922 Vietnamese legal questions in terms of recall and NDCG.

PhoBERT: Pre-trained language models for Vietnamese (2020.findings-emnlp)

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Dat Quoc Nguyen, Anh Tuan Nguyen

SemViQA: A Semantic Question Answering System for Vietnamese Information Fact-Checking (2026.acl-industry)

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.

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Dien X. Tran, Nam V. Nguyen, Tran Tan Thanh, Anh T. Hoang, Dương Văn Tài, Lê Thanh Di, Phuc-Lu Le

Ethical NLP

Challenge:	Existing methods struggle with semantic ambiguity, homonyms, and complex linguistic structures, often trading accuracy for efficiency.
Approach:	They propose a Vietnamese fact-checking framework that integrates SER and TVC to achieve 78.97% strict accuracy.
Outcome:	The proposed framework achieves state-of-the-art accuracy with 78.97% strict accuracy on ISE-DSC01 and 80.82% on ViWikiFC while maintaining competitive accuracy.

Adaptive Knowledge Sharing in Multi-Task Learning: Improving Low-Resource Neural Machine Translation (P18-2)

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Poorya Zaremoodi, Wray Buntine, Gholamreza Haffari

Low-Resource NLP

Challenge:	Neural Machine Translation (NMT) requires large amounts of bilingual data to learn a translation model with reasonable quality.
Approach:	They propose to extend recurrent units with multiple "blocks" along with a trainable "routing network" this allows for adaptive collaboration by dynamic sharing of blocks conditioned on the task at hand, input, and model state.
Outcome:	Empirical evaluations of two low-resource translation tasks show +1 BLEU score improvements compared to strong baselines.

Which Works Best for Vietnamese? A Practical Study of Information Retrieval Methods across Domains (2026.findings-eacl)

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Long S. T. Nguyen, Tho T. Quan

Information Retrieval

Challenge:	Existing studies on Large Language Models (LLMs) are limited to single domains or curated datasets.
Approach:	They propose a domain-normalized, multi-domain benchmark for Vietnamese IR . they evaluate lexical, neural-sparse, late-interaction, dense, and hybrid paradigms .
Outcome:	The proposed benchmarks cover six domains and ten datasets across education, legal, healthcare, customer support, lifestyle reviews, and open-domain knowledge.

GigaSpeech 2: An Evolving, Large-Scale and Multi-domain ASR Corpus for Low-Resource Languages with Automated Crawling, Transcription and Refinement (2025.acl-long)

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Yifan Yang, Zheshu Song, Jianheng Zhuo, Mingyu Cui, Jinpeng Li, Bo Yang, Yexing Du, Ziyang Ma, Xunying Liu, Ziyuan Wang, Ke Li, Shuai Fan, Kai Yu, Wei-Qiang Zhang, Guoguo Chen, Xie Chen

Efficient Convolutional Neural Networks for Diacritic Restoration (D19-1)

Challenge:	GigaSpeech 2 is a large-scale, multi-domain, multilingual speech recognition corpus for low-resource languages.
Approach:	They propose a large-scale, multi-domain, multilingual speech recognition corpus for low-resource languages and an automated pipeline for data crawling, transcription, and label refinement.
Outcome:	The proposed corpus reduces the word error rate for Thai, Indonesian, and Vietnamese on a realistic YouTube test set by 25% to 40% compared to Whisper large-v3.

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Sawsan Alqahtani, Ajay Mishra, Mona Diab

A-TASC: Asian TED-Based Automatic Subtitling Corpus (2025.acl-long)

Challenge:	Diacritic restoration is a computational task that requires a computer to understand written texts.
Approach:	They propose to use Temporal Convolutional Neural Networks (TCN) to restore missing diacritics for each character in written text.
Outcome:	The proposed model improves on TCN in Arabic, Yoruba, and Vietnamese.

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Yuhan Zhou, Naoki Yoshinaga

COVID-19 Named Entity Recognition for Vietnamese (2021.naacl-main)

Challenge:	Existing AS corpora and primary metric SubER focus on European languages.
Approach:	They propose an Asian TED-based automatic subtitling corpus derived from English TED Talks and a modification of SubER to enable reliable evaluation of subtitle quality for languages without explicit word boundaries.
Outcome:	The proposed corpus is based on TED Talks audio segments, transcripts, and subtitles in Chinese, Japanese, Korean, and Vietnamese.

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Thinh Hung Truong, Mai Hoang Dao, Dat Quoc Nguyen

Crossing Linguistic Horizons: Finetuning and Comprehensive Evaluation of Vietnamese Large Language Models (2024.findings-naacl)

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.

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Sang Truong, Duc Nguyen, Toan Nguyen, Dong Le, Nhi Truong, Tho Quan, Sanmi Koyejo

This Land is Your, My Land: Evaluating Geopolitical Bias in Language Models through Territorial Disputes (2024.naacl-long)

Challenge:	Existing open-source LLMs exhibit limited effectiveness in processing Vietnamese . lack of systematic benchmark datasets and metrics tailored for Vietnamese LLM evaluation exacerbates these issues.
Approach:	They propose to fine tune LLMs specifically for Vietnamese and develop a framework for evaluation . they find that larger models introduce more biases and uncalibrated outputs .
Outcome:	The proposed framework finetunes LLMs specifically for Vietnamese and provides a framework for evaluation .

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Bryan Li, Samar Haider, Chris Callison-Burch

BKEE: Pioneering Event Extraction in the Vietnamese Language (2024.lrec-main)

Challenge:	Pretrained large language models may answer differently in different languages . this contrasts with a multilingual human, who would likely answer consistently .
Approach:	They propose a dataset of territorial disputes which includes multiple-choice questions in 49 languages . they propose metrics to quantify bias and consistency in responses across different languages based on their data .
Outcome:	The proposed model recalls certain knowledge inconsistently when asked in different languages.

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Thi-Nhung Nguyen, Bang Tien Tran, Trong-Nghia Luu, Thien Huu Nguyen, Kiem-Hieu Nguyen

Enriching Biomedical Knowledge for Low-resource Language Through Large-scale Translation (2023.eacl-main)

Challenge:	Event Extraction (EE) is a fundamental task in information extraction.
Approach:	They propose a Vietnamese event extraction dataset that includes 33 different event types and 28 different event argument roles.
Outcome:	The proposed dataset provides a labeled dataset for entity mentions, event mentions and event arguments on 1066 documents.

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Long Phan, Tai Dang, Hieu Tran, Trieu H. Trinh, Vy Phan, Lam D. Chau, Minh-Thang Luong

Text Generation

Challenge:	Biomedical data and benchmarks are highly valuable but limited in low-resource languages such as English.
Approach:	They propose a translation model in Vietnamese that trains a pretrained Encoder-Decoder Transformer model on 20 million translated abstracts.
Outcome:	The proposed model can translate and produce both pretrained and supervised biomedical data in two biomedically important domains.

A Vietnamese Dataset for Evaluating Machine Reading Comprehension (2020.coling-main)

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Kiet Nguyen, Vu Nguyen, Anh Nguyen, Ngan Nguyen

Reasoning

Challenge:	despite the lack of benchmark datasets for Vietnamese, there are few studies on machine reading comprehension (MRC) . MRC is an essential core for a range of natural language processing applications such as search engines and intelligent agents.
Approach:	They propose to use Vietnamese Question Answering Dataset to evaluate machine reading comprehension in Vietnamese . they use over 23,000 human-generated question-answer pairs based on 5,109 Vietnamese articles .
Outcome:	The proposed dataset includes over 23,000 human-generated question-answer pairs based on 5,109 passages of 174 Vietnamese articles from Wikipedia.

Encoding of lexical tone in self-supervised models of spoken language (2024.naacl-long)

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Gaofei Shen, Michaela Watkins, Afra Alishahi, Arianna Bisazza, Grzegorz Chrupała

Cross-lingual Machine Reading Comprehension with Language Branch Knowledge Distillation (2020.coling-main)

Challenge:	Existing research on representations of phonetic and phonological information has focused on segmental features such as phonemes.
Approach:	They propose to analyze the tone encoding capabilities of self-supervised Spoken Language Models, using Mandarin and Vietnamese as case studies.
Outcome:	The proposed models encode lexical tone even when trained on non-tonal languages.

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Junhao Liu, Linjun Shou, Jian Pei, Ming Gong, Min Yang, Daxin Jiang

An Empirical Investigation of Error Types in Vietnamese Parsing (C18-1)

Challenge:	Cross-lingual Machine Reading Comprehension (CLMRC) is a challenging problem due to the lack of large-scale annotated datasets in low-source languages, such as Arabic, Hindi, and Vietnamese.
Approach:	They propose a novel approach to augment cross-lingual machine reading comprehension by combining knowledge from multiple language branch models into a single model for all target languages.
Outcome:	Extensive experiments on two CLMRC benchmarks show the proposed method is effective and robust to data noises.

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Quy Nguyen, Yusuke Miyao, Hiroshi Noji, Nhung Nguyen

ViGLUE: A Vietnamese General Language Understanding Benchmark and Analysis of Vietnamese Language Models (2024.findings-naacl)

Challenge:	Syntactic parsing improves the quality of natural language processing tasks.
Approach:	They evaluated Vietnamese Treebank model to find most suitable parsing method . they found that Vietnamese parsers produced limited training data and POS errors .
Outcome:	The proposed method improves the parsing quality in Vietnamese . the results highlight three possible sources of parser errors .

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Minh-Nam Tran, Phu-Vinh Nguyen, Long Nguyen, Dien Dinh

MaGiX: A Multi-Granular Adaptive Graph Intelligence Framework for Enhancing Cross-Lingual RAG (2025.findings-emnlp)

Challenge:	Existing benchmarks for natural language understanding have been suggested, but there is a lack of such a benchmark in Vietnamese due to the difficulty in accessing datasets or the scarcity of task-specific datasets.
Approach:	They propose to use a benchmark to evaluate Vietnamese language models in a variety of tasks and areas to explore the relationship between specific tasks and the number of shots.
Outcome:	The proposed benchmark contains twelve tasks and encompasses over ten areas and subjects, enabling it to evaluate models comprehensively over a broad spectrum of aspects.

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Nguyen Manh Hieu, Vu Lam Anh, Hung Pham Van, Nam Le Hai, Linh Ngo Van, Nguyen Thi Ngoc Diep, Thien Huu Nguyen

Recipes for Adapting Pre-trained Monolingual and Multilingual Models to Machine Translation (2021.eacl-main)

Challenge:	Recent advances in Graph-based RAG (GRAG) frameworks focus on knowledge graphs for cross-lingual retrieval.
Approach:	They propose a new GRAG framework for cross-lingual question answering . MaGiX constructs a multi-granular cross-linguistic knowledge graph using fine-grained attribute descriptions and cross-synonym edges.
Outcome:	The proposed framework outperforms prior GRAG systems in retrieval accuracy and generation quality.

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Asa Cooper Stickland, Xian Li, Marjan Ghazvininejad

ViSoBERT: A Pre-Trained Language Model for Vietnamese Social Media Text Processing (2023.emnlp-main)

Challenge:	Recent advances in machine translation (MT) have improved performance on low-resource language pairs.
Approach:	They propose to freeze most BART parameters and add new ones to fine-tune a model trained on MT.
Outcome:	The proposed model outperforms naive fine-tuning on Vietnamese to English on a training set for Vietnamese to Vietnamese . the proposed model is able to fine- tune on smaller datasets while still maintaining the same model performance.

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Nam Nguyen, Thang Phan, Duc-Vu Nguyen, Kiet Nguyen

ViNLI: A Vietnamese Corpus for Studies on Open-Domain Natural Language Inference (2022.coling-1)

Challenge:	English and Chinese have seen the strong development of transformer-based language models for natural language processing tasks.
Approach:	They present a monolingual pre-trained language model for Vietnamese social media texts . they explore emotion recognition, hate speech detection, sentiment analysis, spam reviews detection .
Outcome:	The proposed model outperforms the existing models on Vietnamese social media tasks with fewer parameters.

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Tin Van Huynh, Kiet Van Nguyen, Ngan Luu-Thuy Nguyen

Textual Inference

Challenge:	a large-scale corpus is needed for studies on natural language inference (NLI) for Vietnamese, which can be considered a low-resource language.
Approach:	They propose a corpus for evaluating Vietnamese natural language inference models . they use a human-annotated corpus extracted from more than 800 online news articles .
Outcome:	The ViNLI corpus is created and evaluated with a strict process of quality control . the best system performance is still far from human performance (a 14.20% gap in accuracy).

BKTreebank: Building a Vietnamese Dependency Treebank (L18-1)

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Kiem-Hieu Nguyen

Vietnamese Automatic Speech Recognition: A Revisit (2026.findings-eacl)

Challenge:	In this paper, we present the building of a dependency treebank for Vietnamese .
Approach:	They propose to build a Vietnamese dependency treebank using automatic taggers and automatic tagging.
Outcome:	The proposed treebank is a useful resource for Vietnamese language processing.

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Thi Vu, Linh The Nguyen, Dat Quoc Nguyen

Bayesian Learning for Neural Dependency Parsing (N19-1)

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.

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Ehsan Shareghi, Yingzhen Li, Yi Zhu, Roi Reichart, Anna Korhonen

ViHateT5: Enhancing Hate Speech Detection in Vietnamese With a Unified Text-to-Text Transformer Model (2024.findings-acl)

Challenge:	Several approaches for dependency parsing in the small data regime have been proposed.
Approach:	They propose to use stochastic gradient Langevin dynamics to generate samples from the approximated posterior to overcome the computational and statistical costs of the approximate inference step.
Outcome:	The proposed model outperforms the biaffine model on 6 languages with less than 5k training instances and improves across five languages.

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Luan Thanh Nguyen

A Pilot Study of Text-to-SQL Semantic Parsing for Vietnamese (2020.findings-emnlp)

Challenge:	Existing methods for HSD in Vietnamese focus on fine-tuning general pre-trained models, primarily trained on formal textual datasets like Wikipedia.
Approach:	They propose a T5-based HSD model pre-trained on a domain-specific dataset . their results highlight the significance of label distribution in pre-training data on model efficacy.
Outcome:	The proposed model can tackle multiple tasks using a unified model and achieve state-of-the-art performance across all standard HSD benchmarks in Vietnamese.

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Anh Tuan Nguyen, Mai Hoang Dao, Dat Quoc Nguyen

XRICL: Cross-lingual Retrieval-Augmented In-Context Learning for Cross-lingual Text-to-SQL Semantic Parsing (2022.findings-emnlp)

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.

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Peng Shi, Rui Zhang, He Bai, Jimmy Lin

A Named Entity Recognition Corpus for Vietnamese Biomedical Texts to Support Tuberculosis Treatment (2022.lrec-1)

Challenge:	Existing work focuses on English datasets, and it is unclear whether large language models can serve as competitive semantic parsers for other languages.
Approach:	They propose a framework that learns to retrieve relevant English exemplars for a given query to construct prompts.
Outcome:	The proposed framework learns to retrieve relevant English exemplars for a given query to construct prompts.

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Uyen Phan, Phuong N.V Nguyen, Nhung Nguyen

Low-Resource NLP

Challenge:	Named Entity Recognition (NER) is an important task in information extraction.
Approach:	They construct a labelled NER corpus of Vietnamese academic biomedical text . they annotate documents with five categories of named entities: Organisation, Location, Date and Time, Symptom and Disease, and Diagnostic Procedure.
Outcome:	The proposed system could provide answers to questions related to TB in Vietnamese . the system could also be used to identify TB-related diseases in the country .

Linking the TUFS Basic Vocabulary to the Open Multilingual Wordnet (2020.lrec-1)

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Francis Bond, Hiroki Nomoto, Luís Morgado da Costa, Arthur Bond

Combining Discourse Markers and Cross-lingual Embeddings for Synonym–Antonym Classification (N19-1)

Challenge:	The TUFS Basic Vocabulary Modules are hand created, using commonly occurring vocabulary.
Approach:	They propose to link the TUFS Basic Vocabulary Modules with the Open Multilingual Wordnet to create a multilingual lexicon.
Outcome:	The proposed lexicons can be used to evaluate existing wordnets, add data to wordnet synsets and create new open wordnet for Khmer, Korean, Lao, Mongolian, Russian, Tagalog, Urdua nd Vietnamese.

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Michael Roth, Shyam Upadhyay

A Fast and Accurate Vietnamese Word Segmenter (L18-1)

Challenge:	Recent work shows that distributional semantic approaches have difficulty distinguishing between synonyms and antonyms.
Approach:	They propose to use monolingual distributional information available in a target language to transfer supervision to other languages using cross-lingual word embeddings.
Outcome:	The proposed method improves the transfer of monolingual distributional information to other languages using co-occurrences with discourse markers indicative of antonymy.

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Dat Quoc Nguyen, Dai Quoc Nguyen, Thanh Vu, Mark Dras, Mark Johnson

Multi-Dialect Vietnamese: Task, Dataset, Baseline Models and Challenges (2024.emnlp-main)

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.

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Nguyen Dinh, Thanh Dang, Luan Thanh Nguyen, Kiet Nguyen

When Morphology Hides in Plain Sight: Breaking the Isolation in Vietnamese and Beyond (2026.acl-long)

Challenge:	Vietnamese is a low-resource language, but each province has its own distinct pronunciation variations.
Approach:	They propose a dataset that captures the rich diversity of 63 provincial dialects spoken in Vietnam.
Outcome:	The proposed dataset captures the rich diversity of 63 provincial dialects spoken across Vietnam.

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Anh Trac Duc Dinh, Khang Hoang Nhat Vo, Tai Tien Ta, Vinh Cong Doan, Tho Quan

Improving Low-Resource Named Entity Recognition using Joint Sentence and Token Labeling (2020.acl-main)

Challenge:	Adaptive Boundary-Token Fusion and a Morpheme-Aware Attention Bias are used to encode monosyllabic morphemes.
Approach:	They propose a morpheme-aware Transformer that augments a pretrained Vietnamese encoder with two lightweight inductive biases.
Outcome:	The proposed morpheme-aware Transformer outperforms strong baselines on Vietnamese POS, NER, and sentence-level classification benchmarks.

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Canasai Kruengkrai, Thien Hai Nguyen, Sharifah Mahani Aljunied, Lidong Bing

A Simple and Effective Approach to Robust Unsupervised Bilingual Dictionary Induction (2020.coling-main)

Challenge:	Existing models for named entity recognition (NER) use sentence-level labels, which are expensive to obtain, to improve NER.
Approach:	They propose a sentence-level named entity recognition model that uses sentence-based labels that are easy to obtain.
Outcome:	The proposed model produces 3.78%, 4.20%, 2.08% improvements in F1 over the baseline on e-commerce product titles in Vietnamese, Thai, and Indonesian, respectively.

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Yanyang Li, Yingfeng Luo, Ye Lin, Quan Du, Huizhen Wang, Shujian Huang, Tong Xiao, Jingbo Zhu

DivEMT: Neural Machine Translation Post-Editing Effort Across Typologically Diverse Languages (2022.emnlp-main)

Challenge:	Recent work has questioned the robustness of unsupervised bilingual dictionary induction methods on distant language pairs.
Approach:	They propose an iterative dimension reduction method to bridge this gap . they propose a method that initializes and self-learning and inducing a dictionary .
Outcome:	The proposed method achieves 13.64 55.53% accuracy between English and four distant languages.

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Gabriele Sarti, Arianna Bisazza, Ana Guerberof-Arenas, Antonio Toral

VMLU Benchmarks: A comprehensive benchmark toolkit for Vietnamese LLMs (2025.acl-long)

Challenge:	Recent advances in neural language modeling and multilingual training have prompted widespread adoption of machine translation (MT) technologies across an unprecedented range of world languages.
Approach:	They propose to use a dataset to assess the impact of two state-of-the-art NMT systems, Google Translate and the multilingual mBART-50 model, on translation productivity.
Outcome:	The proposed model is faster than translation from scratch, but the magnitude of productivity gains varies widely across systems and languages.

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Cuc Thi Bui, Nguyen Truong Son, Truong Van Trang, Lam Viet Phung, Pham Nhut Huy, Hoang Anh Le, Quoc Huu Van, Phong Nguyen-Thuan Do, Van Le Tran Truc, Duc Thanh Chau, Le-Minh Nguyen

Challenge:	The evolution of Large Language Models (LLMs) has underscored the need for benchmarks designed for various languages and cultural contexts.
Approach:	They propose to use Vietnamese multitask language understanding (VMLU) benchmarks to assess different capabilities of LLMs, including general knowledge, reading comprehension, reasoning, and conversational skills.
Outcome:	The VMLU Benchmarks assess LLMs' general knowledge, reading comprehension, reasoning, and conversational skills.

KC4MT: A High-Quality Corpus for Multilingual Machine Translation (2022.lrec-1)

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Vinh Van Nguyen, Ha Nguyen, Huong Thanh Le, Thai Phuong Nguyen, Tan Van Bui, Luan Nghia Pham, Anh Tuan Phan, Cong Hoang-Minh Nguyen, Viet Hong Tran, Anh Huu Tran

MultiMed-ST: Large-scale Many-to-many Multilingual Medical Speech Translation (2025.emnlp-main)

Challenge:	In machine translation, Vietnamese is a low-resource language, and the quality of the training corpus is very low.
Approach:	They propose a method for building high-quality multilingual parallel corpus in news domain . they also publicize a corpus that includes 500.000 Vietnamese-Chinese bilingual sentence pairs .
Outcome:	The proposed method improves the quality of multilingual machine translation in Vietnamese, Laos, and Khmer . the public version includes 500.000 Vietnamese-Chinese bilingual sentence pairs .

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Khai Le-Duc, Tuyen Tran, Bach Phan Tat, Nguyen Kim Hai Bui, Quan Dang Anh, Hung-Phong Tran, Thanh Thuy Nguyen, Ly Nguyen, Tuan Minh Phan, Thi Thu Phuong Tran, Chris Ngo, Khanh Xuan Nguyen, Thanh Nguyen-Tang

A Vietnamese Dialog Act Corpus Based on ISO 24617-2 standard (L18-1)

Challenge:	Multilingual speech translation (ST) and machine translation (MT) in the medical domain enhances patient care by enabling efficient communication across language barriers.
Approach:	They present a large-scale ST dataset for the medical domain spanning all translation directions in Vietnamese, English, German, French, and Simplified/Traditional Chinese, together with the models.
Outcome:	The multi-language speech translation (ST) and machine translation (MT) in the medical domain is the largest medical MT dataset and the largest many-to-many multilingual ST among all domains.

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Thi-Lan Ngo, Pham Khac Linh, Hideaki Takeda

Dialogue Systems & Conversational Agents

Challenge:	standardized dialog act corpora are used for conversation mining research . different corporations often use different methods to understand interaction structure .
Approach:	They propose to annotate dialog acts using ISO 24617-2 standard (2012) . they also annotated emotions using Ekman's six primitives and sentiment using tags "positive", "negative" and "neutral"
Outcome:	The proposed corpus is constructed using the ISO 24617-2 standard (2012) . it is used for emotions, sentiment and positive, negative and neutral tags .

SEA-HELM: Southeast Asian Holistic Evaluation of Language Models (2025.findings-acl)

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Yosephine Susanto, Adithya Venkatadri Hulagadri, Jann Railey Montalan, Jian Gang Ngui, Xianbin Yong, Wei Qi Leong, Hamsawardhini Rengarajan, Peerat Limkonchotiwat, Yifan Mai, William Chandra Tjhi

MLQA: Evaluating Cross-lingual Extractive Question Answering (2020.acl-main)

Challenge:	Existing LLM benchmarks are capable of evaluating specific capabilities in English as well as in various mid- to low-resource languages, but a comprehensive and culturally representative evaluation suite for the SEA languages has not been developed thus far.
Approach:	They propose a holistic linguistic and cultural LLM evaluation suite that emphasizes SEA languages and introduces a leaderboard that allows users to understand models’ multilingual and multicultural performance.
Outcome:	The proposed evaluation suite emphasizes SEA languages and supports Filipino, Indonesian, Tamil, Thai, and Vietnamese.

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Patrick Lewis, Barlas Oguz, Ruty Rinott, Sebastian Riedel, Holger Schwenk

Question Answering

Challenge:	Question answering (QA) models have shown rapid progress enabled by the availability of large, high-quality benchmark datasets.
Approach:	They present a multi-way aligned extractive QA evaluation benchmark in 7 languages . they evaluate state-of-the-art cross-lingual models and machine-translation-based baselines .
Outcome:	The proposed model is based on MLQA, which has over 12K instances in english and 5K in each other language.

VIMQA: A Vietnamese Dataset for Advanced Reasoning and Explainable Multi-hop Question Answering (2022.lrec-1)

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Khang Le, Hien Nguyen, Tung Le Thanh, Minh Nguyen

Revisiting Tri-training of Dependency Parsers (2021.emnlp-main)

Challenge:	Existing Vietnamese Question Answering (QA) datasets do not explore the model’s ability to perform advanced reasoning and provide evidence to explain the answer.
Approach:	They propose to use Vietnamese as a question-answer dataset with 10,000 Wikipedia-based multi-hop question-and-answ pairs to test model's ability to reason and explain the answer.
Outcome:	The proposed dataset is in Vietnamese, a low-resource language.

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Joachim Wagner, Jennifer Foster

Using Convolution Neural Network with BERT for Stance Detection in Vietnamese (2022.lrec-1)

Challenge:	Pre-trained word embeddings and self-training have been used in dependency parsing tasks for years.
Approach:	They compare tri-training and pretrained word embeddings in dependency parsing . they use language-specific FastText and ELMo embedds and multilingual BERT embedders .
Outcome:	The proposed methods are tri-training and pretrained word embeddings.

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Oanh Tran, Anh Cong Phung, Bach Xuan Ngo

A Parallel Corpus for Vietnamese Central-Northern Dialect Text Transfer (2023.findings-emnlp)

Challenge:	Stance detection is a task of automatically eliciting stance information towards a specific claim made by a primary author.
Approach:	They propose an architecture using transformers to detect stances in Vietnamese claims . they exploit BERT to extract contextual word embeddings instead of traditional word2vec models .
Outcome:	The proposed model outperforms the previous methods on a public dataset.

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Thang Le, Anh Luu

Challenge:	Among these, the northern dialect is often treated as the standard i.e. the defacto text style of the language.
Approach:	They propose a parallel corpus for Vietnamese central-northern dialect text transfer to facilitate research on this domain.
Outcome:	The proposed model improves existing models on the central dialect domain with dedicated results in translation and text-image retrieval tasks.

OWL: Probing Cross-Lingual Recall of Memorized Texts via World Literature (2025.emnlp-main)

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Alisha Srivastava, Emir Kaan Korukluoglu, Minh Nhat Le, Duyen Tran, Chau Minh Pham, Marzena Karpinska, Mohit Iyyer

VietMed: A Dataset and Benchmark for Automatic Speech Recognition of Vietnamese in the Medical Domain (2024.lrec-main)

Challenge:	Large language models (LLMs) are known to memorize and recall English text from their pretraining data, but the extent to which this ability generalizes to non-English languages or transfers across languages remains unclear.
Approach:	They propose a dataset of 31.5K aligned excerpts from 20 books in ten languages, including English originals, official translations and new translations in six low-resource languages.
Outcome:	The proposed model can recall English content in translations, but perturbations reduce performance, causing the model to fail.

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Khai Le-Duc

Translating Domain-Specific Terminology in Typologically-Diverse Languages: A Study in Tax and Financial Education (2025.emnlp-main)

Challenge:	Currently, there are no publicly available speech recognition datasets in the medical domain due to privacy restrictions.
Approach:	They present a Vietnamese speech recognition dataset in the medical domain comprising 16h of labeled medical speech, 1000h of unlabeled medical and 1200h of general-domain speech.
Outcome:	The proposed model outperforms state-of-the-art models from 51.8% to 29.6% WER on test set.

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Arturo Oncevay, Elena Kochkina, Keshav Ramani, Toyin Aguda, Simerjot Kaur, Charese Smiley

ViLegalLM: Language Models for Vietnamese Legal Text (2026.findings-acl)

Challenge:	Existing public terminology datasets for MT research are limited in language coverage or domain specificity, making it difficult to assess or improve MT systems in specialized settings.
Approach:	They propose a multilingual terminology resource for tax and financial education covering seven typologically diverse languages: English, Spanish, Russian, Vietnamese, Korean, Chinese (traditional and simplified) and Haitian Creole.
Outcome:	The proposed terminology resource covers seven typologically diverse languages: English, Spanish, Russian, Vietnamese, Korean, Chinese (traditional and simplified) and Haitian Creole.

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Truong-Phuc Nguyen, Quy-Nhan Nguyen, Minh-Tien Nguyen