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--- |
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language: |
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- en |
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license: [] |
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multilinguality: |
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- monolingual |
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pretty_name: KVRET |
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size_categories: |
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- 1K<n<10K |
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task_categories: |
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- conversational |
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--- |
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# Dataset Card for KVRET |
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- **Repository:** https://nlp.stanford.edu/blog/a-new-multi-turn-multi-domain-task-oriented-dialogue-dataset/ |
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- **Paper:** https://arxiv.org/pdf/1705.05414.pdf |
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- **Leaderboard:** None |
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- **Who transforms the dataset:** Qi Zhu(zhuq96 at gmail dot com) |
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To use this dataset, you need to install [ConvLab-3](https://github.com/ConvLab/ConvLab-3) platform first. Then you can load the dataset via: |
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``` |
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from convlab.util import load_dataset, load_ontology, load_database |
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dataset = load_dataset('kvret') |
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ontology = load_ontology('kvret') |
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database = load_database('kvret') |
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``` |
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For more usage please refer to [here](https://github.com/ConvLab/ConvLab-3/tree/master/data/unified_datasets). |
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### Dataset Summary |
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In an effort to help alleviate this problem, we release a corpus of 3,031 multi-turn dialogues in three distinct domains appropriate for an in-car assistant: calendar scheduling, weather information retrieval, and point-of-interest navigation. Our dialogues are grounded through knowledge bases ensuring that they are versatile in their natural language without being completely free form. |
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- **How to get the transformed data from original data:** |
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- Run `python preprocess.py` in the current directory. |
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- **Main changes of the transformation:** |
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- Create user `dialogue acts` and `state` according to original annotation. |
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- Put dialogue level kb into system side `db_results`. |
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- Skip repeated turns and empty dialogue. |
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- **Annotations:** |
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- user dialogue acts, state, db_results. |
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### Supported Tasks and Leaderboards |
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NLU, DST, Context-to-response |
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### Languages |
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English |
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### Data Splits |
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| 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) | |
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|------------|-------------|--------------|-----------|--------------|---------------|-------------------------|------------------------|--------------------------------|-----------------------------------| |
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| train | 2424 | 12720 | 5.25 | 8.02 | 1 | - | - | - | 98.07 | |
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| validation | 302 | 1566 | 5.19 | 7.93 | 1 | - | - | - | 97.62 | |
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| test | 304 | 1627 | 5.35 | 7.7 | 1 | - | - | - | 97.72 | |
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| all | 3030 | 15913 | 5.25 | 7.98 | 1 | - | - | - | 97.99 | |
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3 domains: ['schedule', 'weather', 'navigate'] |
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- **cat slot match**: how many values of categorical slots are in the possible values of ontology in percentage. |
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- **non-cat slot span**: how many values of non-categorical slots have span annotation in percentage. |
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### Citation |
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``` |
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@inproceedings{eric-etal-2017-key, |
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title = "Key-Value Retrieval Networks for Task-Oriented Dialogue", |
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author = "Eric, Mihail and |
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Krishnan, Lakshmi and |
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Charette, Francois and |
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Manning, Christopher D.", |
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booktitle = "Proceedings of the 18th Annual {SIG}dial Meeting on Discourse and Dialogue", |
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year = "2017", |
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publisher = "Association for Computational Linguistics", |
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url = "https://aclanthology.org/W17-5506", |
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} |
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``` |
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### Licensing Information |
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TODO |
