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YAML Metadata Warning:The task_categories "conversational" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other

Dataset Card for WOZ 2.0

To use this dataset, you need to install ConvLab-3 platform first. Then you can load the dataset via:

from convlab.util import load_dataset, load_ontology, load_database

dataset = load_dataset('woz')
ontology = load_ontology('woz')
database = load_database('woz')

For more usage please refer to here.

Dataset Summary

Describe the dataset.

  • How to get the transformed data from original data:

  • Main changes of the transformation:

    • domain is set to restaurant.
    • normalize the value of categorical slots in state and dialogue acts.
    • belief_states in WOZ dataset contains request intents, which are ignored in processing.
    • use simple string match to find value spans of non-categorical slots.
  • Annotations:

    • User dialogue acts, state

Supported Tasks and Leaderboards

NLU, DST, E2E

Languages

English

Data Splits

split dialogues utterances avg_utt avg_tokens avg_domains cat slot match(state) cat slot match(goal) cat slot match(dialogue act) non-cat slot span(dialogue act)
train 600 4472 7.45 11.37 1 100 - 100 96.56
validation 200 1460 7.3 11.28 1 100 - 100 95.52
test 400 2892 7.23 11.49 1 100 - 100 94.83
all 1200 8824 7.35 11.39 1 100 - 100 95.83

1 domains: ['restaurant']

  • cat slot match: how many values of categorical slots are in the possible values of ontology in percentage.
  • non-cat slot span: how many values of non-categorical slots have span annotation in percentage.

Citation

@inproceedings{mrksic-etal-2017-neural,
    title = "Neural Belief Tracker: Data-Driven Dialogue State Tracking",
    author = "Mrk{\v{s}}i{\'c}, Nikola  and
      {\'O} S{\'e}aghdha, Diarmuid  and
      Wen, Tsung-Hsien  and
      Thomson, Blaise  and
      Young, Steve",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/P17-1163",
    doi = "10.18653/v1/P17-1163",
    pages = "1777--1788",
}

Licensing Information

Apache License, Version 2.0

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