---
title: "Which Works Best for Vietnamese? A Practical Study of"
description: "Retrieval quality limits what a RAG-based question answering system can achieve. 37 references."
authors: ["Long S. T. Nguyen", "Tho T. Quan"]
year: 2026
venue: "Findings of the Association for Computational Linguistics: EACL 2026"
source: "https://aclanthology.org/2026.findings-eacl.110/"
doi: "10.18653/v1/2026.findings-eacl.110"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/nguyen-2026-which-works-best-vietnamese-practical/"
---
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# Which Works Best for Vietnamese? A Practical Study of Information Retrieval Methods across Domains

**Authors:** [Long S. T. Nguyen](https://md.research.elamur.ai/free/en/authors/long-s-t-nguyen/) and [Tho T. Quan](https://md.research.elamur.ai/free/en/authors/tho-quan/) · **Year:** 2026 · **Venue:** [Findings of the Association for Computational Linguistics: EACL 2026](https://md.research.elamur.ai/free/en/venues/findings/) ([Findings of the Association for Computational Linguistics 2026](https://md.research.elamur.ai/free/en/venues/findings/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.findings-eacl.110/) · [PDF](https://aclanthology.org/2026.findings-eacl.110.pdf) · [DOI 10.18653/v1/2026.findings-eacl.110](https://doi.org/10.18653/v1/2026.findings-eacl.110) · [OpenAlex](https://openalex.org/W7140127371)

**Citations:** 2 citations (OpenAlex, 2026-10-14)

## Summary
Retrieval quality limits what a RAG-based question answering system can achieve. For Vietnamese, however, nobody had compared retrieval approaches across many domains in one consistent setup. Earlier work usually tested a single area, such as law, or used clean curated QA sets.

The authors built a benchmark of ten datasets spread over six domains: open-domain knowledge, healthcare, the legal field, education, reviews of lifestyle topics, and support for customers. Each dataset was reduced to the same standardized size so that domains can be compared fairly. They tested lexical, neural-sparse, late-interaction, dense, and hybrid retrievers in a zero-shot setting, using both Vietnamese-specific and multilingual encoders, including LLM-based embedders. They also released two new noisy, real-world datasets, EduCoQA and CSConDa.

Three findings stand out:
- Combining BM25 with dense scores through linear fusion was the most dependable choice across domains.
- Vietnamese-specialized encoders did well on formal institutional text, while multilingual ones held up better on conversational data.
- Bigger models did not reliably retrieve better.

These results give practical guidance for building Vietnamese RAG systems, and the benchmark offers a reproducible base for future work.

## Key points
- **Benchmark scope:** six domains and ten datasets, each capped at 1,000 query–document pairs. Code and data are hosted in the ViRE GitHub repository.
- **New datasets:** EduCoQA comes from educational counseling and CSConDa from customer-service conversations. Both were anonymized and are released under CC BY-NC 4.0.
- **Hybrid methods tested:** α-weighted score interpolation and Reciprocal Rank Fusion (RRF). BM25-based linear fusion was the strongest overall.
- **Difficulty gap:** legal and encyclopedic corpora are close to saturation. EduCoQA, CSConDa, VlogQA, and ViRe4MRC remain hard because of slang, missing diacritics, register mismatch, and ambiguous intent.
- **Limitations:** the study covers only text-only, single-hop, zero-shot retrieval. Fine-tuned retrievers and multimodal or speech retrieval are left for future work.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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## Cite

Cite as: Long S. T. Nguyen and Tho T. Quan. 2026. Which Works Best for Vietnamese? A Practical Study of Information Retrieval Methods across Domains. Findings of the Association for Computational Linguistics: EACL 2026.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{nguyen2026which,
  title = {{Which Works Best for Vietnamese? A Practical Study of Information Retrieval Methods across Domains}},
  author = {Long S. T. Nguyen and Tho T. Quan},
  booktitle = {Findings of the Association for Computational Linguistics: EACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-eacl.110},
  url = {https://doi.org/10.18653/v1/2026.findings-eacl.110}
}
```

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## References

37 references, 13 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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*Updated 2 November 2026*