real-jiakai/chinese-squadv2

English | 中文

Dataset Card for Chinese SQuAD 2.0 (revised, bilingual)

Dataset Description

This is a revised and extended version of the Chinese translation of SQuAD 2.0, originally machine-translated by ChineseSquad. Like SQuAD 2.0 it contains both answerable and unanswerable questions and is designed for Chinese extractive reading comprehension / question answering.

Compared with the previous release of chinese-squadv2, this version:

1. Adds the original English SQuAD 2.0 fields (title_en, context_en, question_en, answers_en, is_impossible), aligned to every example by the original SQuAD id. For the validation split, answers_en restores all human reference answers from the official dev set (deduplicated), not just one.
2. Reviews and corrects the Chinese machine translation of all 99,963 pre-existing examples. Every unique paragraph together with its questions and answers was checked against the English original by LLMs (grok-3-mini-fast: 9,797 paragraph groups, gemini-3-flash: 9,731, qwen3.6-flash: 3, kimi-k2: 1) with a constrained-correction protocol: corrections were accepted only if every Chinese answer remains a verbatim substring of the (possibly corrected) Chinese context, and answer_start offsets were recomputed programmatically. Typical fixed errors: mistranslated terminology (e.g. 教会-图灵论点 -> 丘奇-图灵论题, 胶带 -> 纸带), wrong senses (维多利亚时代 -> 维多利亚州), untranslated fragments (张R -> 张柔), and garbled sentences.
3. Recovers 42,055 of the 42,229 examples that the original ChineseSquad project dropped (answerable questions whose answer spans could not be aligned after 2019-era machine translation). They were re-translated with the same LLM pool under the same substring constraint: where the paragraph already had a (corrected) Chinese context, only the question and answer span were translated against that fixed context; 174 examples whose answers still could not be aligned remain excluded and are listed in corrections/unrecovered_*.jsonl.
4. Ships a full audit log (corrections/) listing every changed example with old/new text and the model that produced it, so all edits can be reviewed.

Dataset Structure

{
    'id': string,                 # original SQuAD 2.0 id
    'title': string,              # Chinese article title
    'context': string,            # Chinese paragraph (revised)
    'question': string,           # Chinese question (revised)
    'answers': {'text': List[string], 'answer_start': List[int]},   # in Chinese context
    'title_en': string,           # original English title
    'context_en': string,         # original English paragraph
    'question_en': string,        # original English question
    'answers_en': {'text': List[string], 'answer_start': List[int]}, # in English context
    'is_impossible': bool,        # True = unanswerable (both answers lists empty)
    'translation_source': string, # 'chinesesquad-revised' | 'llm-recovered'
}

Unanswerable questions have empty answers / answers_en lists, consistent with the squad_v2 convention. Rows are ordered following the official English SQuAD 2.0 file order.

Data Splits

Split	Examples	Answerable	Unanswerable	of which recovered	Unique paragraphs
train	130,162	86,664	43,498	40,135	19,029
validation	11,856	5,911	5,945	1,920	1,204

Coverage vs the official English SQuAD 2.0: 130,162/130,319 train and 11,856/11,873 dev examples (157 + 17 examples could not be recovered; see corrections/unrecovered_*.jsonl). All ids map 1:1 into the official English SQuAD 2.0.

Revision statistics (pre-existing examples)

Split	Examples changed	Context revised	Question revised	Answer text revised	Verified unchanged
train	76,791	72,984	25,055	2,515	13,236
validation	8,846	8,480	3,005	310	1,090

answer_start offsets were recomputed for every example whose context changed. All 19,532 unique paragraph groups were successfully reviewed; no example was left unverified in this release. The span invariant (every Chinese answer appears verbatim at its answer_start in its Chinese context) holds for 100% of examples.

Usage

from datasets import load_dataset

dataset = load_dataset("real-jiakai/chinese-squadv2")

example = dataset['train'][0]
print(example['question'])      # Chinese question
print(example['question_en'])   # original English question
print(example['answers'])       # answer span in the Chinese context
print(example['answers_en'])    # answer span(s) in the English context

# only the original (revised) ChineseSquad subset:
subset = dataset.filter(lambda e: e['translation_source'] == 'chinesesquad-revised')

Revision process (August 12-13, 2026)

This revision was produced as a human + AI collaboration: the dataset author directed the work (scope, model selection, rate limits, review policy, publication), while Claude Fable 5 (Anthropic), running as an autonomous coding agent in the Cursor CLI, wrote and operated all of the code. Translation and review were delegated to external LLMs behind an OpenAI-compatible proxy; the agent handled orchestration, validation and assembly. The steps were:

1. Alignment audit. The previous parquet release, the original ChineseSquad GitHub JSON and the official English SQuAD 2.0 files were cross-checked: all 99,963 pre-existing examples map 1:1 by id into official SQuAD 2.0, and all answer spans were mechanically valid before revision.
2. English supplementation. The English fields were joined by id as a pure data step (no LLM involved), restoring all human reference answers for the validation split.
3. Translation review. All 19,532 unique paragraph groups (paragraph + its questions/answers) were reviewed - one request per group - by grok-3-mini-fast (9,797 groups) and gemini-3-flash (9,731), with qwen3.6-flash (3) and kimi-k2 (1) as fallbacks; doubao-seed-2.1-turbo was configured but automatically disabled after repeated gateway timeouts. Models returned strict-JSON verdicts with minimal corrections. A correction was accepted only if every Chinese answer remained a verbatim substring of the final Chinese context; answer_start offsets were always recomputed programmatically, never taken from the model.
4. Recovery of dropped examples. The 42,229 examples dropped by the 2019 project were re-translated under the same substring constraint (grok-3-mini-fast: 8,933 groups, gemini-3-flash: 8,818, kimi-k2: 1, qwen3.6-flash: 1). Where the paragraph already had a corrected Chinese context (95%+ of cases) the context was kept fixed and only the question and answer span were translated; 701 paragraphs were translated from scratch. 42,055 examples were recovered; 174 whose answers could not be aligned remain excluded.
5. Pipeline engineering. The workflow is implemented as ~10 Python scripts: a concurrent HTTP client with a global 1-request/second start-rate cap, round-robin model rotation with automatic failure-based restriction, resumable JSONL checkpointing, JSON-schema and substring validation with cross-model retries, merge/reorder to the official SQuAD file order, and full-dataset invariant checks (span integrity and answerability flags hold for 100% of the 142,018 published rows).

In total the pipelines issued ~37,300 successful paragraph-group requests over roughly 10 hours of wall-clock time.

Citation

If you use this dataset, please cite the original SQuAD papers and the Chinese translation project:

@inproceedings{rajpurkar-etal-2018-know,
    title = "Know What You Don{'}t Know: Unanswerable Questions for {SQ}u{AD}",
    author = "Rajpurkar, Pranav and Jia, Robin and Liang, Percy",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    year = "2018",
    url = "https://aclanthology.org/P18-2124",
    doi = "10.18653/v1/P18-2124",
    pages = "784--789"
}

@inproceedings{rajpurkar-etal-2016-squad,
    title = "{SQ}u{AD}: 100,000+ Questions for Machine Comprehension of Text",
    author = "Rajpurkar, Pranav and Zhang, Jian and Lopyrev, Konstantin and Liang, Percy",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    year = "2016",
    url = "https://aclanthology.org/D16-1264",
    doi = "10.18653/v1/D16-1264",
    pages = "2383--2392"
}

@misc{ChineseSquad,
    title = "ChineseSquad",
    author = "junzeng-pluto",
    url = "https://github.com/junzeng-pluto/ChineseSquad"
}

License

CC BY-SA 4.0, following the original SQuAD 2.0 license.

Limitations

• Translations were machine-produced and machine-reviewed; residual errors are possible. The audit log allows targeted human review.
• 174 examples of the official SQuAD 2.0 could not be aligned and remain excluded (corrections/unrecovered_*.jsonl).
• The Chinese answers field keeps a single reference answer per answerable question (the English answers_en field carries all references for validation).
• Recovered examples (translation_source == 'llm-recovered') were translated by LLMs in 2026 and did not go through the original ChineseSquad pipeline.

Acknowledgements

• ChineseSquad by junzeng-pluto, the original machine translation this dataset builds on.
• The SQuAD authors for the original English dataset.
• Claude Fable 5 (Anthropic), running in the Cursor CLI, which performed the alignment analysis, wrote and operated the review/recovery pipelines, validated the results and assembled this release as an autonomous coding agent, in collaboration with the dataset author.
• grok-3-mini-fast, gemini-3-flash, qwen3.6-flash and kimi-k2, the models that performed the translation review and recovery translations.