Challenge: POS taggers are trained on informal texts which contain many informal inputs such as acronyms, abbreviations, out-of-vocabulary words, etc.
Approach: They propose a large-scale human-labeled dataset for the Vietnamese POS tagging task on conversational texts and develop an annotation guideline to manually annotate 16.310K sentences using this guideline.
Outcome: The proposed tagging scheme achieved 93.36% accuracy score and higher than the model with handcrafted features and fine-tuning BERT.

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What data should I include in my POS tagging training set? (2025.findings-emnlp)

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Challenge: POS tagging is a crucial task for descriptive linguistics and language documentation . POS tags are not available in all languages, but are used for training sets for understudied languages .
Approach: They compare POS tagging with in-context learning, active learning, and random sampling . they find that POS can deliver reasonable results for communities with limited resources .
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Handling Normalization Issues for Part-of-Speech Tagging of Online Conversational Text (L18-1)

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Challenge: a new approach to POS tagging noisy user generated text is proposed . word embeddings are trained on a noisy corpus to address both normalization and POS.
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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 .
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Bridging Pre-trained Language Models and Hand-crafted Features for Unsupervised POS Tagging (2022.findings-acl)

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Challenge: Large-scale pre-trained language models (PLMs) have made extraordinary progress in most NLP tasks, but they fail to achieve state-of-the-art (SOTA) performance.
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VN-MTEB: Vietnamese Massive Text Embedding Benchmark (2026.findings-eacl)

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

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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 .
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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.
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Multi-Dialect Arabic POS Tagging: A CRF Approach (L18-1)

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Challenge: Existing work on dialectal POS tagging is rather scant with POS tags for most dialects being nonexistent or of limited availability.
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When Speed Meets Intelligence: Scalable Conversational NER in an Ever-evolving World (2026.eacl-industry)

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Challenge: Large Language Models excel at understanding conversational semantics, but lack of data makes them impractical for production deployment.
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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 .
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