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. 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. 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. 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. 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

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)

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. 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. 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. 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. 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. 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.