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--- |
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language: |
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- en |
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license: |
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- cc-by-4.0 |
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multilinguality: |
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- monolingual |
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pretty_name: Camrest |
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size_categories: |
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- n<1K |
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task_categories: |
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- conversational |
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--- |
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# Dataset Card for Camrest |
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- **Repository:** https://www.repository.cam.ac.uk/handle/1810/260970 |
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- **Paper:** https://aclanthology.org/D16-1233/ |
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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('camrest') |
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ontology = load_ontology('camrest') |
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database = load_database('camrest') |
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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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Cambridge restaurant dialogue domain dataset collected for developing neural network based dialogue systems. The two papers published based on this dataset are: 1. A Network-based End-to-End Trainable Task-oriented Dialogue System 2. Conditional Generation and Snapshot Learning in Neural Dialogue Systems. The dataset was collected based on the Wizard of Oz experiment on Amazon MTurk. Each dialogue contains a goal label and several exchanges between a customer and the system. Each user turn was labelled by a set of slot-value pairs representing a coarse representation of dialogue state (`slu` field). There are in total 676 dialogue, in which most of the dialogues are finished but some of dialogues were not. |
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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. Need `../../camrest/` as the original data. |
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- **Main changes of the transformation:** |
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- Add dialogue act annotation according to the state change. This step was done by ConvLab-2 and we use the processed dialog acts here. |
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- Rename `pricerange` to `price range` |
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- Add character level span annotation for non-categorical slots. |
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- **Annotations:** |
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- user goal, dialogue acts, state. |
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### Supported Tasks and Leaderboards |
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NLU, DST, Policy, NLG, E2E, User simulator |
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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 | 406 | 3342 | 8.23 | 10.6 | 1 | 100 | 100 | 100 | 99.83 | |
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| validation | 135 | 1076 | 7.97 | 11.26 | 1 | 100 | 100 | 100 | 100 | |
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| test | 135 | 1070 | 7.93 | 11.01 | 1 | 100 | 100 | 100 | 100 | |
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| all | 676 | 5488 | 8.12 | 10.81 | 1 | 100 | 100 | 100 | 99.9 | |
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1 domains: ['restaurant'] |
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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{wen-etal-2016-conditional, |
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title = "Conditional Generation and Snapshot Learning in Neural Dialogue Systems", |
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author = "Wen, Tsung-Hsien and Ga{\v{s}}i{\'c}, Milica and Mrk{\v{s}}i{\'c}, Nikola and Rojas-Barahona, Lina M. and Su, Pei-Hao and Ultes, Stefan and Vandyke, David and Young, Steve", |
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booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing", |
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month = nov, |
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year = "2016", |
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address = "Austin, Texas", |
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publisher = "Association for Computational Linguistics", |
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url = "https://aclanthology.org/D16-1233", |
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doi = "10.18653/v1/D16-1233", |
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pages = "2153--2162", |
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} |
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``` |
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### Licensing Information |
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[**CC BY 4.0**](https://creativecommons.org/licenses/by/4.0/) |
