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README.md

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    Dataset Card for [dataset name]

    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