---
title: "LCQMC:A Large-scale Chinese Question Matching Corpus (2018)"
description: "Large manually labelled corpora for question matching are scarce, especially outside English. 31 references, cited by 51 papers here."
authors: ["Xin Liu", "Qingcai Chen", "Chong Deng", "Huajun Zeng", "Jing Chen", "Dongfang Li", "Buzhou Tang"]
year: 2018
venue: "Proceedings of the 27th International Conference on Computational Linguistics"
source: "https://aclanthology.org/C18-1166/"
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/liu-2018-lcqmc-large-scale-chinese-question/"
---
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# LCQMC:A Large-scale Chinese Question Matching Corpus

**Authors:** [Xin Liu](https://md.research.elamur.ai/free/en/authors/xin-liu/), [Qingcai Chen](https://md.research.elamur.ai/free/en/authors/qingcai-chen/), Chong Deng, Huajun Zeng, [Jing Chen](https://md.research.elamur.ai/free/en/authors/jing-chen/), Dongfang Li and [Buzhou Tang](https://md.research.elamur.ai/free/en/authors/buzhou-tang/) · **Year:** 2018 · **Venue:** [Proceedings of the 27th International Conference on Computational Linguistics](https://md.research.elamur.ai/free/en/venues/coling/) ([COLING 2018](https://md.research.elamur.ai/free/en/venues/coling/2018/)) · **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/C18-1166/) · [PDF](https://aclanthology.org/C18-1166.pdf) · [OpenAlex](https://openalex.org/W2876111955)

**Citations:** 113 citations (OpenAlex, 2026-10-14) · [51](https://md.research.elamur.ai/free/en/papers/liu-2018-lcqmc-large-scale-chinese-question/cited-by/) in our library, of the 141,840 papers whose bibliographies we read

## Summary
Large manually labelled corpora for question matching are scarce, especially outside English. This limits deep learning methods that decide whether two questions express the same intent. The authors address this for Chinese with LCQMC, a public corpus that targets shared intent rather than strict paraphrase. They mined Baidu Knows, a large Chinese community QA site full of duplicate questions worded in different ways. High-frequency seed words from many domains were sent as queries, and the seed list was expanded using tf.idf. Questions returned for the same seed were paired. Word mover distance (WMD), a Wasserstein-based measure, then reduced several million candidates to a manageable set, which three annotators labelled by hand. The final resource has 260,068 labelled pairs split into train, dev and test sets. Experiments with several known unsupervised and supervised sentence matching methods support the corpus's quality and give reference baselines. Its scale and open availability make it a useful benchmark for Chinese semantic matching and QA.

## Key points
- **Size and splits:** LCQMC has 260,068 question pairs, divided into 238,766 for training, 8,802 for development and 12,500 for testing.
- **Collection and filtering:**
  - About 50 seed words were chosen per domain, covering areas such as education, games, sports and science.
  - The top 100 result pages were crawled for each seed, then the seed list was expanded with tf.idf and the top 50 pages were kept for each new seed.
  - This produced over 5 million candidate pairs, which WMD filtering narrowed before human checking.
- **Lexical overlap:** Average word overlap within pairs is about 75%, compared with roughly 70% in MSRP and 60% in ULPC. Matching pairs with under 50% overlap make up only about 2.5% of the corpus.
- **Hard negatives:** Because WMD keeps only lexically close pairs, many non-matching pairs differ by just a few words. This makes them difficult for models to separate.
- **Matching types:** A manual review of 1,000 sampled pairs identified five recurring types: intent-based, elaboration, phrasal, synonymy and reordering. Together these account for about 44% of the sample.

*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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- [Microsoft](https://md.research.elamur.ai/free/en/companies/microsoft/) — Company, Redmond, United States · 240 works · 123,537 citations
- [Harbin Institute of Technology](https://md.research.elamur.ai/free/en/universities/harbin-institute-of-technology/) — University, Harbin, China · 76 works · 27,918 citations

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## Cite

Cite as: Xin Liu, Qingcai Chen, Chong Deng, Huajun Zeng, Jing Chen, Dongfang Li and Buzhou Tang. 2018. LCQMC:A Large-scale Chinese Question Matching Corpus. Proceedings of the 27th International Conference on Computational Linguistics.

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

```bibtex
@inproceedings{liu2018lcqmc,
  title = {{LCQMC:A Large-scale Chinese Question Matching Corpus}},
  author = {Xin Liu, Qingcai Chen, Chong Deng, Huajun Zeng, Jing Chen, Dongfang Li and Buzhou Tang},
  booktitle = {Proceedings of the 27th International Conference on Computational Linguistics},
  year = {2018},
  url = {https://aclanthology.org/C18-1166/}
}
```

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*Updated 24 October 2026*