| --- |
| language: |
| - en |
| license: [] |
| multilinguality: |
| - monolingual |
| pretty_name: KVRET |
| size_categories: |
| - 1K<n<10K |
| task_categories: |
| - conversational |
| --- |
| |
| # Dataset Card for KVRET |
|
|
| - **Repository:** https://nlp.stanford.edu/blog/a-new-multi-turn-multi-domain-task-oriented-dialogue-dataset/ |
| - **Paper:** https://arxiv.org/pdf/1705.05414.pdf |
| - **Leaderboard:** None |
| - **Who transforms the dataset:** Qi Zhu(zhuq96 at gmail dot com) |
|
|
| 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: |
| ``` |
| from convlab.util import load_dataset, load_ontology, load_database |
| |
| dataset = load_dataset('kvret') |
| ontology = load_ontology('kvret') |
| database = load_database('kvret') |
| ``` |
| For more usage please refer to [here](https://github.com/ConvLab/ConvLab-3/tree/master/data/unified_datasets). |
|
|
| ### Dataset Summary |
|
|
| 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. |
|
|
| - **How to get the transformed data from original data:** |
| - Run `python preprocess.py` in the current directory. |
| - **Main changes of the transformation:** |
| - Create user `dialogue acts` and `state` according to original annotation. |
| - Put dialogue level kb into system side `db_results`. |
| - Skip repeated turns and empty dialogue. |
| - **Annotations:** |
| - user dialogue acts, state, db_results. |
| |
| ### Supported Tasks and Leaderboards |
| |
| NLU, DST, Context-to-response |
| |
| ### 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 | 2424 | 12720 | 5.25 | 8.02 | 1 | - | - | - | 98.07 | |
| | validation | 302 | 1566 | 5.19 | 7.93 | 1 | - | - | - | 97.62 | |
| | test | 304 | 1627 | 5.35 | 7.7 | 1 | - | - | - | 97.72 | |
| | all | 3030 | 15913 | 5.25 | 7.98 | 1 | - | - | - | 97.99 | |
|
|
| 3 domains: ['schedule', 'weather', 'navigate'] |
| - **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{eric-etal-2017-key, |
| title = "Key-Value Retrieval Networks for Task-Oriented Dialogue", |
| author = "Eric, Mihail and |
| Krishnan, Lakshmi and |
| Charette, Francois and |
| Manning, Christopher D.", |
| booktitle = "Proceedings of the 18th Annual {SIG}dial Meeting on Discourse and Dialogue", |
| year = "2017", |
| publisher = "Association for Computational Linguistics", |
| url = "https://aclanthology.org/W17-5506", |
| } |
| ``` |
|
|
| ### Licensing Information |
|
|
| TODO |
