metadata

dataset_info:
  - config_name: default
    features:
      - name: premise
        dtype: large_string
      - name: hypothesis
        dtype: large_string
      - name: template_num
        dtype: int64
      - name: time_format
        dtype: large_string
      - name: time_span
        dtype: large_string
      - name: category
        dtype: large_string
      - name: label
        dtype:
          class_label:
            names:
              '0': entailment
              '1': neutral
              '2': contradiction
    splits:
      - name: train
        num_bytes: 2424590
        num_examples: 9950
      - name: test
        num_bytes: 88516
        num_examples: 348
    download_size: 594545
    dataset_size: 2513106
  - config_name: template
    features:
      - name: id
        dtype: int64
      - name: premise
        dtype: large_string
      - name: hypothesis
        dtype: large_string
      - name: entailment
        dtype: large_string
      - name: contradiction
        dtype: large_string
      - name: ng time unit
        dtype: large_string
      - name: test time format
        dtype: large_string
      - name: category
        dtype: large_string
    splits:
      - name: train
        num_bytes: 26196
        num_examples: 79
    download_size: 9709
    dataset_size: 26196
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: test
        path: data/test-*
  - config_name: template
    data_files:
      - split: train
        path: template/train-*
license: cc-by-sa-4.0
task_categories:
  - text-classification
language:
  - ja
tags:
  - nli
  - evaluation
  - benchmark
pretty_name: >-
  Jamp: Controlled Japanese Temporal Inference Dataset for Evaluating
  Generalization Capacity of Language Models

Jamp: Controlled Japanese Temporal Inference Dataset for Evaluating Generalization Capacity of Language Models

Jamp(tomo-vv/temporalNLI_dataset) is the Japanese temporal inference benchmark. This dataset consists of templates, test data, and training data.

Template subset containing template, time format, or time span in their names are split based on tense fragment, time format, or time span, respectively.

Dataset Details

Dataset Description

Created by: tomo-vv([email protected])
Language(s) (NLP): Japanese
License: CC BY-SA 4.0

Dataset Sources

Repository: tomo-vv/temporalNLI_dataset
Paper: Jamp: Controlled Japanese Temporal Inference Dataset for Evaluating Generalization Capacity of Language Models (Sugimoto et al., ACL 2023)

Citation

BibTeX:

@inproceedings{sugimoto-etal-2023-jamp,
    title = "Jamp: Controlled {J}apanese Temporal Inference Dataset for Evaluating Generalization Capacity of Language Models",
    author = "Sugimoto, Tomoki  and
      Onoe, Yasumasa  and
      Yanaka, Hitomi",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.acl-srw.8",
    pages = "57--68",
}

APA:

Sugimoto, T., Onoe, Y., & Yanaka, H. (2023). Jamp: Controlled Japanese Temporal Inference Dataset for Evaluating Generalization Capacity of Language Models. arXiv preprint arXiv:2306.10727.