---
dataset_info:
- config_name: v1
  features:
  - name: qid
    dtype: string
  - name: type
    dtype: string
  - name: question
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  - name: answer
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  - name: derivations
    sequence:
    - name: '0'
      dtype: string
    - name: '1'
      dtype: string
    - name: '2'
      sequence: string
  - name: page_ids
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  - name: time_dependent
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  splits:
  - name: train
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  - name: validation
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    num_examples: 120
  download_size: 216443
  dataset_size: 388115
- config_name: v1.1
  features:
  - name: qid
    dtype: string
  - name: type
    dtype: string
  - name: question
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  - name: answer
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  - name: derivations
    sequence:
    - name: '0'
      dtype: string
    - name: '1'
      dtype: string
    - name: '2'
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  - name: page_ids
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  - name: time_dependent
    dtype: bool
  splits:
  - name: train
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    num_examples: 1059
  - name: validation
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    num_examples: 120
  download_size: 216429
  dataset_size: 388263
configs:
- config_name: v1
  data_files:
  - split: train
    path: v1/train-*
  - split: validation
    path: v1/validation-*
- config_name: v1.1
  data_files:
  - split: train
    path: v1.1/train-*
  - split: validation
    path: v1.1/validation-*
---

# JEMHopQA

> JEMHopQA (Japanese Explainable Multi-hop Question Answering) is a Japanese multi-hop QA dataset that can evaluate internal reasoning. It is a task that takes a question as input and generates an answer and derivations. Derivations are a set of derivation steps and is a semi-structured representation of relationships between entities. This dataset contains both compositional (linking information from two Wikipedia articles) and comparison (comparing information from two Wikipedia articles) questions.

Source: [JEMHopQA on GitHub](https://github.com/aiishii/JEMHopQA)


### Licensing Information

[Creative Commons Attribution Share Alike 4.0 International](https://github.com/aiishii/JEMHopQA/blob/main/LICENSE)

### Citation Information

```
@inproceedings{ishii-etal-2024-jemhopqa-dataset,
    title = "{JEMH}op{QA}: Dataset for {J}apanese Explainable Multi-Hop Question Answering",
    author = "Ishii, Ai  and
      Inoue, Naoya  and
      Suzuki, Hisami  and
      Sekine, Satoshi",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.831",
    pages = "9515--9525",
}
```