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supervised_data

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Sentence Similarity
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Vietnamese
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---
pretty_name: HUIT-Embedding supervised triplets (Vietnamese)
language:
- vi
license: other
task_categories:
- sentence-similarity
- feature-extraction
size_categories:
- 1M<n<10M
configs:
- config_name: vinli
data_files:
- split: train
path: data/vinli/train.parquet
- config_name: vianli
data_files:
- split: train
path: data/vianli/train.parquet
- config_name: vinli_simcse_v2
data_files:
- split: train
path: data/vinli_simcse_v2/train.parquet
- config_name: mmarco_vi
data_files:
- split: train
path: data/mmarco_vi/train.parquet
- config_name: vimqa
data_files:
- split: train
path: data/vimqa/train.parquet
- config_name: vlogqa
data_files:
- split: train
path: data/vlogqa/train.parquet
- config_name: vire4mrc
data_files:
- split: train
path: data/vire4mrc/train.parquet
- config_name: virhe4qa
data_files:
- split: train
path: data/virhe4qa/train.parquet
- config_name: multi_wiki_qa
data_files:
- split: train
path: data/multi_wiki_qa/train.parquet
- config_name: vimedaqa
data_files:
- split: train
path: data/vimedaqa/train.parquet
- config_name: vihealthqa
data_files:
- split: train
path: data/vihealthqa/train.parquet
- config_name: vicov19qa
data_files:
- split: train
path: data/vicov19qa/train.parquet
- config_name: csconda
data_files:
- split: train
path: data/csconda/train.parquet
- config_name: vifactcheck
data_files:
- split: train
path: data/vifactcheck/train.parquet
- config_name: viwikifc
data_files:
- split: train
path: data/viwikifc/train.parquet
- config_name: isedsc2024
data_files:
- split: train
path: data/isedsc2024/train.parquet
- config_name: vietnamese_law_hn_mined
data_files:
- split: train
path: data/vietnamese_law_hn_mined/train.parquet
- config_name: large_vi_legal_queries
data_files:
- split: train
path: data/large_vi_legal_queries/train.parquet
- config_name: table_markdown_retrieval
data_files:
- split: train
path: data/table_markdown_retrieval/train.parquet
---
# HUIT-Embedding — supervised data (Vietnamese)
(query, positive, negatives[]) rows for fine-tuning Vietnamese embedding models: 19 non-empty sources, **1,827,077 rows**. Processed on 2026-10-21. Only negatives that ship with the source are included; mined hard negatives are in separate repositories (see Hard-negative variants). Rows that overlap evaluation data are removed.
## Usage
```python
from datasets import load_dataset
ds = load_dataset("QuangDuy/supervised_data", "vinli", split="train")
```
## Schema
| Column | Type | Meaning |
|---|---|---|
| `query` | string | Question, claim or premise |
| `positive` | string | Relevant document or entailed sentence |
| `negatives` | list[string] | Negatives provided by the source |
| `negative_type` | list[string] | `contradiction`, `refuted_evidence`, `distractor` or `provided` |
| `source` | string | Config name |
| `label_origin` | string | `human_paper`, `competition`, `translated` or `synthetic` |
| `translated` | bool | Machine-translated text |
| `group_id` | string | Rows sharing a query or document |
| `license` | string | Source license |
## Sources
| Config | Source | Paper | License | Label origin | Translated | Rows in | Rows out | Kept | Queries | Avg negatives | Query p50/p95 | Positive p50/p95 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| `vinli` | [presencesw/vinli_4_label](https://huggingface.co/datasets/presencesw/vinli_4_label) | [paper](https://aclanthology.org/2022.coling-1.339/) | unknown | human_paper | False | 6,094 | 4,228 | 69.4% | 2,107 | 2.013 | 32/61 | 21/37 |
| `vianli` | [uitnlp/ViANLI](https://huggingface.co/datasets/uitnlp/ViANLI) | [paper](https://doi.org/10.1016/j.eswa.2025.130109) | mit | human_paper | False | 2,615 | 1,657 | 63.4% | 1,606 | 0.916 | 32/59 | 16/31 |
| `vinli_simcse_v2` | [anti-ai/ViNLI-SimCSE-supervised_v2](https://huggingface.co/datasets/anti-ai/ViNLI-SimCSE-supervised_v2) | [paper](https://aclanthology.org/N18-1101/) | mit | translated | True | 275,601 | 269,934 | 97.9% | 268,952 | 0.992 | 18/54 | 10/23 |
| `mmarco_vi` | [unicamp-dl/mmarco](https://huggingface.co/datasets/unicamp-dl/mmarco), [GreenNode/msmarco-vn](https://huggingface.co/datasets/GreenNode/msmarco-vn) | [paper](https://arxiv.org/abs/2108.13897) | apache-2.0 (repo); passages from GreenNode/msmarco-vn cc-by-sa-4.0; MS MARCO terms (non-commercial) | translated | True | 532,743 | 509,371 | 95.6% | 470,747 | 0.0 | 8/15 | 78/152 |
| `viquad2` | [taidng/UIT-ViQuAD2.0](https://huggingface.co/datasets/taidng/UIT-ViQuAD2.0) | [paper](https://doi.org/10.25073/2588-1086/vnucsce.340) | unknown | human_paper | False | 19,238 | 0 | 0.0% | 0 | 0.0 | 0/0 | 0/0 |
| `vimqa` | [nguyenlab/vimqa](https://huggingface.co/datasets/nguyenlab/vimqa) | [paper](https://aclanthology.org/2022.lrec-1.700/) | cc-by-nc-sa-4.0 | human_paper | False | 14,588 | 13,050 | 89.5% | 7,159 | 8.057 | 19/31 | 132/350 |
| `vlogqa` | [uitnlp/vlogqa](https://huggingface.co/datasets/uitnlp/vlogqa) | [paper](https://aclanthology.org/2024.eacl-long.79/) | cc-by-sa-4.0 | human_paper | False | 8,386 | 3,846 | 45.9% | 3,839 | 0.0 | 12/18 | 3428/7889 |
| `vire4mrc` | <https://github.com/DoPhamPhucTinh/ViRe4MRC/raw/main/ViRe4MRC.zip> | [paper](https://aclanthology.org/2023.paclic-1.3/) | unknown (README: research purposes only, not for commercial use) | human_paper | False | 5,297 | 2,667 | 50.3% | 2,612 | 0.0 | 12/17 | 93/171 |
| `virhe4qa` | [TaiDoan123/UIT-R2GQA](https://huggingface.co/datasets/TaiDoan123/UIT-R2GQA) (config triplet) | [paper](https://arxiv.org/abs/2409.02840) | unknown | human_paper | False | 7,806 | 75 | 1.0% | 75 | 0.013 | 15/25 | 264/613 |
| `multi_wiki_qa` | [alexandrainst/multi-wiki-qa](https://huggingface.co/datasets/alexandrainst/multi-wiki-qa) (config vi) | [paper](https://arxiv.org/abs/2509.04111) | cc-by-nc-sa-4.0 | synthetic | False | 5,000 | 4,932 | 98.6% | 4,932 | 0.0 | 16/27 | 684/4917 |
| `vimedaqa` | [tmnam20/ViMedAQA](https://huggingface.co/datasets/tmnam20/ViMedAQA) | [paper](https://aclanthology.org/2024.acl-srw.31/) | unknown | human_paper | False | 39,881 | 31,486 | 78.9% | 31,437 | 0.0 | 15/25 | 111/312 |
| `vihealthqa` | [tarudesu/ViHealthQA](https://huggingface.co/datasets/tarudesu/ViHealthQA) | [paper](https://arxiv.org/abs/2206.09600) | unknown | human_paper | False | 7,009 | 6,586 | 94.0% | 6,583 | 0.0 | 16/80 | 96/295 |
| `vicov19qa` | <https://raw.githubusercontent.com/triet2397/UIT-ViCoV19QA/main/dataset/4_ans/UIT-ViCoV19QA_train.csv> | [paper](https://aclanthology.org/2022.paclic-1.88/) | unknown (README: research purposes only) | human_paper | False | 5,704 | 4,632 | 81.2% | 2,911 | 0.0 | 30/98 | 84/377 |
| `csconda` | [ura-hcmut/Vietnamese-Customer-Support-QA](https://huggingface.co/datasets/ura-hcmut/Vietnamese-Customer-Support-QA) | [paper](https://doi.org/10.1007/978-3-032-10202-7_33) | cc-by-nc-4.0 | human_paper | False | 8,349 | 20 | 0.2% | 19 | 0.0 | 13/29 | 43/127 |
| `vifactcheck` | [tranthaihoa/vifactcheck](https://huggingface.co/datasets/tranthaihoa/vifactcheck) | [paper](https://arxiv.org/abs/2412.15308) | mit | human_paper | False | 1,751 | 1,596 | 91.1% | 1,595 | 2.482 | 37/65 | 50/111 |
| `viwikifc` | [High-Will/ViWikiFC](https://huggingface.co/datasets/High-Will/ViWikiFC) | [paper](https://aclanthology.org/2026.lrec-1.869/) | cc-by-4.0 | human_paper | False | 5,594 | 1,802 | 32.2% | 1,802 | 0.698 | 25/53 | 36/83 |
| `isedsc2024` | [MiuN2k3/isedsc.train.dataset](https://huggingface.co/datasets/MiuN2k3/isedsc.train.dataset) | – | unknown | competition | False | 8,519 | 7,951 | 93.3% | 7,946 | 0.0 | 22/53 | 28/58 |
| `zalo_legal_triplets` | [batmangiaicuuthegioi/zalo-legal-triplets](https://huggingface.co/datasets/batmangiaicuuthegioi/zalo-legal-triplets) | – | unknown | competition | False | 37,059 | 0 | 0.0% | 0 | 0.0 | 0/0 | 0/0 |
| `zalo_legal` | [GreenNode/zalo-ai-legal-text-retrieval-vn](https://huggingface.co/datasets/GreenNode/zalo-ai-legal-text-retrieval-vn) | – | mit | competition | False | 2,505 | 0 | 0.0% | 0 | 0.0 | 0/0 | 0/0 |
| `vietnamese_law_hn_mined` | [GreenNode/Vietnamese-Law-hn-mined-v2](https://huggingface.co/datasets/GreenNode/Vietnamese-Law-hn-mined-v2) | – | unknown | competition | False | 354,123 | 338,193 | 95.5% | 328,102 | 6.868 | 29/45 | 222/1106 |
| `large_vi_legal_queries` | [phamson02/large-vi-legal-queries](https://huggingface.co/datasets/phamson02/large-vi-legal-queries) | [paper](https://doi.org/10.1007/978-981-96-4291-5_31) | cc-by-4.0 | synthetic | False | 507,152 | 498,454 | 98.3% | 494,476 | 0.0 | 27/40 | 208/266 |
| `table_markdown_retrieval` | [GreenNode/GreenNode-Table-Markdown-Retrieval-VN](https://huggingface.co/datasets/GreenNode/GreenNode-Table-Markdown-Retrieval-VN) | – | mit | synthetic | False | 143,106 | 126,597 | 88.5% | 125,842 | 0.0 | 22/33 | 325/507 |
Notes:
- `viquad2`: almost all rows removed by ViRE decontamination
- `csconda`: almost all rows removed by ViRE decontamination
- `zalo_legal_triplets`: almost all rows removed by ViRE decontamination
- `zalo_legal`: almost all rows removed by ViRE decontamination
- `table_markdown_retrieval`: Label origin is not documented; questions look model-generated.
- Not published (no rows left after decontamination): `viquad2`, `zalo_legal_triplets`, `zalo_legal`
## Rows removed and decontamination
Cleaning, language and statistical filters as for the unsupervised data, dedup on (query, positive), and removal of every row overlapping the evaluation blocklist (all of ViRE, VN-MTEB test data and the test splits of the training sources).
| Config | Rows dropped (reason: count) | Eval overlap by set (top 5) | Negative types |
|---|---|---|---|
| `vinli` | eval_overlap: 1,857, lang: 6, duplicate: 3 | viqpc_test: 1,835, vianli_test: 12, vinli_test: 10 | contradiction: 8,512 |
| `vianli` | eval_overlap: 952, lang: 6 | vianli_test: 932, viqpc_test: 12, vinli_test: 8 | contradiction: 1,518 |
| `vinli_simcse_v2` | lang: 2,665, query_equals_positive: 2,047, stat_repeat: 344, eval_overlap: 289, too_short: 255, duplicate: 42, stat_upper: 12, stat_special: 12, empty: 1 | vnmteb_sickr-sts-vn: 279, vnmteb_stsbenchmark-sts-vn: 10 | contradiction: 267,677 |
| `mmarco_vi` | lang: 21,023, too_short: 1,465, duplicate: 412, eval_overlap: 265, stat_upper: 95, stat_special: 89, stat_repeat: 22, empty: 1 | vnmteb_msmarco-vn: 125, vnmteb_dbpedia-vn: 47, vimqa_test: 22, vnmteb_quora-vn: 22, vnmteb_hotpotqa-vn: 16 | – |
| `viquad2` | eval_overlap: 19,162, lang: 74, too_short: 2 | vire_uit-viquad2: 19,162 | – |
| `vimqa` | eval_overlap: 1,516, lang: 21, stat_upper: 1 | vimqa_test: 1,405, viqpc_test: 60, vinli_test: 31, viwikifc_test: 20 | distractor: 105,142 |
| `vlogqa` | eval_overlap: 4,491, lang: 46, duplicate: 3 | vire_vlogqa_2: 4,433, vlogqa_test: 58 | – |
| `vire4mrc` | eval_overlap: 2,599, lang: 24, duplicate: 4, stat_special: 3 | vire_vire4mrc_v2: 2,522, vire4mrc_test: 77 | – |
| `virhe4qa` | eval_overlap: 7,629, duplicate: 84, stat_upper: 17, lang: 1 | vire_virhe4qa_v2: 7,427, virhe4qa_test: 202 | provided: 1 |
| `multi_wiki_qa` | lang: 37, eval_overlap: 25, stat_repeat: 6 | viqpc_test: 25 | – |
| `vimedaqa` | eval_overlap: 8,114, lang: 174, empty: 76, stat_repeat: 18, too_short: 10, duplicate: 2, stat_upper: 1 | vimedaqa_test: 5,884, vire_vimedaqa_v2: 2,205, vire_vinewsqa: 19, isedsc_dev: 3, viqpc_test: 2 | – |
| `vihealthqa` | eval_overlap: 399, lang: 19, duplicate: 5 | vicov19qa_test: 333, vihealthqa_test: 64, vire_vinewsqa: 1, viqpc_test: 1 | – |
| `vicov19qa` | eval_overlap: 1,019, lang: 38, duplicate: 9, stat_upper: 3, too_short: 2, empty: 1 | vihealthqa_test: 939, vicov19qa_test: 77, vire_alqac: 2, vianli_test: 1 | – |
| `csconda` | eval_overlap: 7,612, lang: 559, too_short: 140, duplicate: 12, stat_upper: 3, stat_special: 1, empty: 1, stat_repeat: 1 | vire_csconda: 7,612 | – |
| `vifactcheck` | query_equals_positive: 118, eval_overlap: 37 | vifactcheck_test: 36, isedsc_dev: 1 | refuted_evidence: 3,962 |
| `viwikifc` | eval_overlap: 3,776, query_equals_positive: 13, duplicate: 2, lang: 1 | viwikifc_test: 3,731, viqpc_test: 43, vire_uit-viquad2: 2 | refuted_evidence: 1,257 |
| `isedsc2024` | duplicate: 216, too_short: 188, eval_overlap: 138, lang: 14, query_equals_positive: 10, stat_repeat: 2 | isedsc_test: 73, isedsc_dev: 62, vifactcheck_test: 3 | refuted_evidence: 1 |
| `zalo_legal_triplets` | eval_overlap: 36,992, duplicate: 65, stat_repeat: 2 | vire_zalolegalqa: 36,992 | – |
| `zalo_legal` | eval_overlap: 2,505 | vire_zalolegalqa: 2,505 | – |
| `vietnamese_law_hn_mined` | eval_overlap: 11,918, duplicate: 820, stat_upper: 25, stat_repeat: 4, lang: 3, too_short: 1 | vnmteb_TVPL-Retrieval-VN: 7,197, hoidap_tvpl_test: 1,530, viqpc_test: 1,355, hoidap_tvpl200k_test: 756, vire_zalolegalqa: 548 | provided: 2,322,749 |
| `large_vi_legal_queries` | eval_overlap: 8,328, stat_upper: 187, stat_special: 98, stat_repeat: 69, lang: 12, duplicate: 4 | vnmteb_TVPL-Retrieval-VN: 2,560, viqpc_test: 2,444, vire_zalolegalqa: 1,842, hoidap_tvpl_test: 716, hoidap_tvpl200k_test: 371 | – |
| `table_markdown_retrieval` | stat_repeat: 12,753, eval_overlap: 1,814, lang: 977, stat_special: 892, stat_upper: 60, empty: 9, too_short: 4 | vnmteb_GreenNode-Table-Markdown-Retrieval-VN: 1,814 | – |
## Composition
By label origin: translated: 42.7%, synthetic: 34.5%, competition: 18.9%, human_paper: 3.9%
## Hard-negative variants
Mined hard negatives for these rows are published separately, one repository per method, so each can be compared in ablations. All use `intfloat/multilingual-e5-large` as retriever and `BAAI/bge-reranker-v2-m3` as teacher, and ship each config twice (texts included, or ids with a documents table).
| Repository | Method | Paper |
|---|---|---|
| [QuangDuy/supervised_data-hn-candidates](https://huggingface.co/datasets/QuangDuy/supervised_data-hn-candidates) | Top-200 retrieval candidates with scores (base of the others; also for SimANS or false-negative masking at training time) | [Multilingual E5](https://arxiv.org/abs/2402.05672) |
| [QuangDuy/supervised_data-hn-percpos](https://huggingface.co/datasets/QuangDuy/supervised_data-hn-percpos) | TopK-PercPos, p = 0.90 / 0.95 / 0.99 and an F2LLM variant | [NV-Retriever](https://arxiv.org/abs/2407.15831), [F2LLM](https://arxiv.org/abs/2510.02294) |
| [QuangDuy/supervised_data-hn-rankwindow](https://huggingface.co/datasets/QuangDuy/supervised_data-hn-rankwindow) | Consistency filter (positive in top 50) and negatives from ranks 30–100 or 50–100 | [KaLM-Embedding](https://arxiv.org/abs/2501.01028) |
| [QuangDuy/supervised_data-hn-rerank-denoise](https://huggingface.co/datasets/QuangDuy/supervised_data-hn-rerank-denoise) | Teacher-scored candidates: near-copy extra positives, flagged false negatives, negatives and all scores for KL distillation | [RocketQA](https://arxiv.org/abs/2010.08191), [RLHN](https://arxiv.org/abs/2505.16967) |
`vire4mrc` and `vlogqa` have no mined negatives: their questions depend on the passage, so top candidates usually answer them too.
## Citations
```bibtex
@inproceedings{huynh-etal-2022-vinli,
title = "{V}i{NLI}: A {V}ietnamese Corpus for Studies on Open-Domain Natural Language Inference",
author = "Huynh, Tin Van and
Nguyen, Kiet Van and
Nguyen, Ngan Luu-Thuy",
editor = "Calzolari, Nicoletta and
Huang, Chu-Ren and
Kim, Hansaem and
Pustejovsky, James and
Wanner, Leo and
Choi, Key-Sun and
Ryu, Pum-Mo and
Chen, Hsin-Hsi and
Donatelli, Lucia and
Ji, Heng and
Kurohashi, Sadao and
Paggio, Patrizia and
Xue, Nianwen and
Kim, Seokhwan and
Hahm, Younggyun and
He, Zhong and
Lee, Tony Kyungil and
Santus, Enrico and
Bond, Francis and
Na, Seung-Hoon",
booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
month = oct,
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2022.coling-1.339/",
pages = "3858--3872"
}
@article{vianli,
title = {A New Benchmark Dataset and Mixture-of-Experts Language Models for Adversarial Natural Language Inference in Vietnamese},
journal = {Expert Systems with Applications},
pages = {130109},
year = {2025},
issn = {0957-4174},
doi = {https://doi.org/10.1016/j.eswa.2025.130109},
url = {https://www.sciencedirect.com/science/article/pii/S095741742503725X},
author = {Tin Van Huynh, Kiet Van Nguyen and Ngan Luu-Thuy Nguyen},
}
@inproceedings{williams-etal-2018-broad,
title = "A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference",
author = "Williams, Adina and
Nangia, Nikita and
Bowman, Samuel R.",
editor = "Walker, Marilyn and
Ji, Heng and
Stent, Amanda",
booktitle = "Proceedings of the 2018 Conference of the {N}orth {A}merican Chapter of the {A}ssociation for {C}omputational {L}inguistics: Human Language Technologies, Volume 1 (Long Papers)",
month = jun,
year = "2018",
address = "New Orleans, Louisiana",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N18-1101/",
doi = "10.18653/v1/N18-1101",
pages = "1112--1122"
}
@misc{bonifacio2022mmarcomultilingualversionms,
title={mMARCO: A Multilingual Version of the MS MARCO Passage Ranking Dataset},
author={Luiz Bonifacio and Vitor Jeronymo and Hugo Queiroz Abonizio and Israel Campiotti and Marzieh Fadaee and Roberto Lotufo and Rodrigo Nogueira},
year={2022},
eprint={2108.13897},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2108.13897},
}
@misc{bajaj2018msmarcohumangenerated,
title={MS MARCO: A Human Generated MAchine Reading COmprehension Dataset},
author={Payal Bajaj and Daniel Campos and Nick Craswell and Li Deng and Jianfeng Gao and Xiaodong Liu and Rangan Majumder and Andrew McNamara and Bhaskar Mitra and Tri Nguyen and Mir Rosenberg and Xia Song and Alina Stoica and Saurabh Tiwary and Tong Wang},
year={2018},
eprint={1611.09268},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/1611.09268},
}
@article{vlsp2021-vimrc,
title={VLSP 2021-ViMRC Challenge: Vietnamese Machine Reading Comprehension},
volume={38},
ISSN={2615-9260},
url={http://dx.doi.org/10.25073/2588-1086/vnucsce.340},
DOI={10.25073/2588-1086/vnucsce.340},
number={2},
journal={VNU Journal of Science: Computer Science and Communication Engineering},
publisher={Vietnam National University Journal of Science},
author={Nguyen, Kiet and Tran, Son Quoc and Nguyen, Luan Thanh and Huynh, Tin Van and Luu, Son Thanh and Nguyen, Ngan Luu-Thuy},
year={2022},
month=dec}
% source: https://huggingface.co/datasets/tarudesu/ViHealthQA (dataset card citation block)
@InProceedings{nguyen2022viheathqa,
author="Nguyen, Nhung Thi-Hong
and Ha, Phuong Phan-Dieu
and Nguyen, Luan Thanh
and Van Nguyen, Kiet
and Nguyen, Ngan Luu-Thuy",
title="SPBERTQA: A Two-Stage Question Answering System Based on Sentence Transformers for Medical Texts",
booktitle="Knowledge Science, Engineering and Management",
year="2022",
publisher="Springer International Publishing",
address="Cham",
pages="371--382",
isbn="978-3-031-10986-7"
}
@inproceedings{nguyen-etal-2020-vietnamese,
title = "A {V}ietnamese Dataset for Evaluating Machine Reading Comprehension",
author = "Van Nguyen, Kiet and
Nguyen, Duc-Vu and
Gia-Tuan Nguyen, Anh and
Nguyen, Ngan Luu-Thuy",
editor = "Scott, Donia and
Bel, Nuria and
Zong, Chengqing",
booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2020.coling-main.233/",
doi = "10.18653/v1/2020.coling-main.233",
pages = "2595--2605"
}
@inproceedings{le-etal-2022-vimqa,
title = "{VIMQA}: A {V}ietnamese Dataset for Advanced Reasoning and Explainable Multi-hop Question Answering",
author = "Le, Nguyen-Khang and
Nguyen, Dieu-Hien and
Le, Tung and
Nguyen, Minh",
editor = "Calzolari, Nicoletta and
B{\'e}chet, Fr{\'e}d{\'e}ric and
Blache, Philippe and
Choukri, Khalid and
Cieri, Christopher and
Declerck, Thierry and
Goggi, Sara and
Isahara, Hitoshi and
Maegaard, Bente and
Mariani, Joseph and
Mazo, H{\'e}l{\`e}ne and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference",
month = jun,
year = "2022",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2022.lrec-1.700/",
pages = "6521--6529"
}
@inproceedings{ngo-etal-2024-vlogqa,
title = "{V}log{QA}: Task, Dataset, and Baseline Models for {V}ietnamese Spoken-Based Machine Reading Comprehension",
author = "Ngo, Thinh and
Dang, Khoa and
Luu, Son and
Nguyen, Kiet and
Nguyen, Ngan Luu-Thuy",
editor = "Graham, Yvette and
Purver, Matthew",
booktitle = "Proceedings of the 18th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = mar,
year = "2024",
address = "St. Julian{'}s, Malta",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.eacl-long.79/",
doi = "10.18653/v1/2024.eacl-long.79",
pages = "1310--1324"
}
@inproceedings{do-etal-2023-machine,
title = "Machine Reading Comprehension for {V}ietnamese Customer Reviews: Task, Corpus and Baseline Models",
author = "Do, Tinh Pham Phuc and
Cao, Ngoc Dinh Duy and
Nguyen, Nhan Thanh and
Huynh, Tin Van and
Nguyen, Kiet Van",
editor = "Huang, Chu-Ren and
Harada, Yasunari and
Kim, Jong-Bok and
Chen, Si and
Hsu, Yu-Yin and
Chersoni, Emmanuele and
A, Pranav and
Zeng, Winnie Huiheng and
Peng, Bo and
Li, Yuxi and
Li, Junlin",
booktitle = "Proceedings of the 37th Pacific {A}sia Conference on Language, Information and Computation",
month = dec,
year = "2023",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.paclic-1.3/",
pages = "24--35"
}
@misc{do2024r2gqaretrieverreadergeneratorquestionanswering,
title={R2GQA: Retriever-Reader-Generator Question Answering System to Support Students Understanding Legal Regulations in Higher Education},
author={Phuc-Tinh Pham Do and Duy-Ngoc Dinh Cao and Khanh Quoc Tran and Kiet Van Nguyen},
year={2024},
eprint={2409.02840},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2409.02840},
}
@misc{smart2026multiwikiqareadingcomprehensionbenchmark,
title={MultiWikiQA: A Reading Comprehension Benchmark in 300+ Languages},
author={Dan Saattrup Smart},
year={2026},
eprint={2509.04111},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2509.04111},
}
@inproceedings{tran-etal-2024-vimedaqa,
title = "{V}i{M}ed{AQA}: A {V}ietnamese Medical Abstractive Question-Answering Dataset and Findings of Large Language Model",
author = "Tran, Minh-Nam and
Nguyen, Phu-Vinh and
Nguyen, Long and
Dinh, Dien",
editor = "Fu, Xiyan and
Fleisig, Eve",
booktitle = "Proceedings of the 62nd Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 4: Student Research Workshop)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.acl-srw.31/",
doi = "10.18653/v1/2024.acl-srw.31",
pages = "252--260",
ISBN = "979-8-89176-097-4"
}
@inproceedings{thai-etal-2022-uit,
title = "{UIT}-{V}i{C}o{V}19{QA}: A Dataset for {COVID}-19 Community-based Question Answering on {V}ietnamese Language",
author = "Thai, Triet and
Thao-Ha, Ngan Chu and
Vo, Anh and
Luu, Son",
editor = "Dita, Shirley and
Trillanes, Arlene and
Lucas, Rochelle Irene",
booktitle = "Proceedings of the 36th Pacific {A}sia Conference on Language, Information and Computation",
month = oct,
year = "2022",
address = "Manila, Philippines",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.paclic-1.88/",
pages = "801--810"
}
@inproceedings{csconda,
title = {A Benchmark Dataset and Evaluation Framework for Vietnamese Large Language Models in Customer Support},
author = {Nguyen, Long S. T. and Hua, Truong P. and Nguyen, Thanh M. and Pham, Toan Q. and Ngo, Nam K. and Nguyen, An X. and Pham, Nghi D. M. and Nguyen, Nghia H. and Quan, Tho T.},
editor = {Nguyen, Ngoc Thanh and Dinh Duc Anh, Vu and Kozierkiewicz, Adrianna and Nguyen Van, Sinh and Nunez, Manuel and Treur, Jan and Vossen, Gottfried},
booktitle = {Advances in Computational Collective Intelligence},
year = {2026},
publisher = {Springer Nature Switzerland},
address = {Cham},
pages = {481--496},
isbn = {978-3-032-10202-7}
}
@inproceedings{vire,
title = "Which Works Best for {V}ietnamese? {A} Practical Study of Information Retrieval Methods across Domains",
author = "Nguyen, Long S. T. and
Quan, Tho T.",
editor = "Demberg, Vera and
Inui, Kentaro and
Marquez, Llu{\'i}s",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EACL} 2026",
month = mar,
year = "2026",
address = "Rabat, Morocco",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-eacl.110/",
doi = "10.18653/v1/2026.findings-eacl.110",
pages = "2098--2119",
ISBN = "979-8-89176-386-9"
}
@inproceedings{tran2025vifactcheck,
title = {ViFactCheck: A New Benchmark Dataset and Methods for Multi-Domain News Fact-Checking in Vietnamese},
author = {Tran, Thai Hoa and Tran, Quang Duy and Tran, Khanh Quoc and Nguyen, Kiet Van},
booktitle = {Proceedings of the Thirty-Ninth AAAI Conference on Artificial Intelligence (AAAI-25)},
pages = {308--316},
year = {2025},
publisher = {AAAI Press}
}
@inproceedings{le-etal-2026-viwikifc,
title = "{V}i{W}iki{FC}: Fact-Checking for {V}ietnamese {W}ikipedia-Based Textual Knowledge Source",
author = "Le, Hung Tuan and
To, Long Truong and
Nguyen, Manh Trong and
Nguyen, Kiet Van",
editor = "Piperidis, Stelios and
Bel, N{\'u}ria and
van den Heuvel, Henk and
Ide, Nancy and
Krek, Simon and
Toral, Antonio",
booktitle = "Proceedings of the Fifteenth Language Resources and Evaluation Conference",
month = may,
year = "2026",
address = "Palma de Mallorca, Spain",
publisher = "ELRA Language Resource Association",
url = "https://aclanthology.org/2026.lrec-1.869/",
doi = "10.63317/5ew98tfds6jc",
pages = "11125--11140"
}
@InProceedings{large-vi-legal-queries,
author="Son, Pham Tien and Hieu, Nguyen Doan and An, Nguyen Dai and Sang, Dinh Viet",
editor="Buntine, Wray and Fjeld, Morten and Tran, Truyen and Tran, Minh-Triet and Huynh Thi Thanh, Binh and Miyoshi, Takumi",
title="Improving Vietnamese Legal Document Retrieval Using Synthetic Data",
booktitle="Information and Communication Technology",
year="2025",
publisher="Springer Nature Singapore",
address="Singapore",
pages="378--393",
isbn="978-981-96-4291-5"
}
@inproceedings{gn-trvn,
abstract = {Information retrieval often comes in plain text, lacking semi-structured text such as HTML and markdown, retrieving data that contains rich format such as table became non-trivial. In this paper, we tackle this challenge by introducing a new dataset, GreenNode Table Retrieval VN (GN-TRVN), which is collected from a massive corpus, a wide range of topics, and a longer context compared to ViQuAD2.0. To evaluate the effectiveness of our proposed dataset, we introduce two versions, M3-GN-VN and M3-GN-VN-Mixed, by fine-tuning the M3-Embedding model on this dataset. Experimental results show that our models consistently outperform the baselines, including the base model, across most evaluation criteria on various datasets such as VieQuADRetrieval, ZacLegalTextRetrieval, and GN-TRVN. In general, we release a more comprehensive dataset and two model versions that improve response performance for Vietnamese Markdown Table Retrieval.},
address = {Singapore},
author = {Pham, Bao Loc
and Hoang, Quoc Viet
and Luu, Quy Tung
and Vo, Trong Thu},
booktitle = {Proceedings of the Fifth International Conference on Intelligent Systems and Networks},
isbn = {978-981-95-1746-6},
pages = {153--163},
publisher = {Springer Nature Singapore},
title = {GN-TRVN: A Benchmark for Vietnamese Table Markdown Retrieval Task},
year = {2026},
}
@misc{vnmteb,
title={VN-MTEB: Vietnamese Massive Text Embedding Benchmark},
author={Loc Pham and Tung Luu and Thu Vo and Minh Nguyen and Viet Hoang},
year={2025},
eprint={2507.21500},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2507.21500},
}
```
No verified citation was found for: `isedsc2024`, `zalo_legal_triplets`, `zalo_legal`, `vietnamese_law_hn_mined`.
## Limitations and licenses
Each source keeps its own license (column above); this collection is `license: other`. Sources that are non-commercial or have no stated license: `vinli`, `viquad2`, `vimqa`, `vire4mrc`, `virhe4qa`, `multi_wiki_qa`, `vimedaqa`, `vihealthqa`, `vicov19qa`, `csconda`, `isedsc2024`, `zalo_legal_triplets`, `vietnamese_law_hn_mined`. Check the original license before any use beyond research. Q&A sources crawled from the web had person names masked with a Vietnamese NER model and phone/ID/account numbers masked by regex (`<name>`, `<phone>`, …).