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README.md

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+---
+license: cc-by-4.0
+pretty_name: Chinese SQuAD for question generation
+language: zh
+multilinguality: monolingual
+size_categories: 10K<n<100K
+task_categories:
+- text-generation
+task_ids:
+- language-modeling
+tags:
+- question-generation
+---
+
+# Dataset Card for "lmqg/qg_zhquad"
+
+## Dataset Description
+- **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation)
+- **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992)
+- **Point of Contact:** [Asahi Ushio](http://asahiushio.com/)
+
+### Dataset Summary
+This is a subset of [QG-Bench](https://github.com/asahi417/lm-question-generation/blob/master/QG_BENCH.md#datasets), a unified question generation benchmark proposed in
+ ["Generative Language Models for Paragraph-Level Question Generation: A Unified Benchmark and Evaluation, EMNLP 2022 main conference"](https://arxiv.org/abs/2210.03992).
+This is a modified version of [Chinese SQuAD](https://github.com/junzeng-pluto/ChineseSquad) for question generation (QG) task.
+Since the original dataset only contains training/validation set, we manually sample test set from training set, which
+has no overlap in terms of the paragraph with the training set.
+
+Please see the original repository ([https://github.com/junzeng-pluto/ChineseSquad](https://github.com/junzeng-pluto/ChineseSquad)) for more details.
+
+### Supported Tasks and Leaderboards
+* `question-generation`: The dataset is assumed to be used to train a model for question generation.
+  Success on this task is typically measured by achieving a high BLEU4/METEOR/ROUGE-L/BERTScore/MoverScore (see our paper for more in detail).
+
+### Languages
+Italian (it)
+
+## Dataset Structure
+The data fields are the same among all splits.
+- `question`: a `string` feature. 
+- `paragraph`: a `string` feature.
+- `answer`: a `string` feature.
+- `sentence`: a `string` feature.
+- `paragraph_answer`: a `string` feature, which is same as the paragraph but the answer is highlighted by a special token `<hl>`.
+- `paragraph_sentence`: a `string` feature, which is same as the paragraph but a sentence containing the answer is highlighted by a special token `<hl>`.
+- `sentence_answer`: a `string` feature, which is same as the sentence but the answer is highlighted by a special token `<hl>`.
+
+Each of `paragraph_answer`, `paragraph_sentence`, and `sentence_answer` feature is assumed to be used to train a question generation model,
+but with different information. The `paragraph_answer` and `sentence_answer` features are for answer-aware question generation and 
+`paragraph_sentence` feature is for sentence-aware question generation.
+
+## Data Splits
+
+|train| validation | test |
+|----:|-----------:|-----:|
+|59977|       8236 | 8236 |
+
+
+## Citation Information
+
+```
+@inproceedings{ushio-etal-2022-generative,
+    title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration",
+    author = "Ushio, Asahi  and
+        Alva-Manchego, Fernando  and
+        Camacho-Collados, Jose",
+    booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
+    month = dec,
+    year = "2022",
+    address = "Abu Dhabi, U.A.E.",
+    publisher = "Association for Computational Linguistics",
+}
+```