Upload README.md

README.md

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+# Dataset Card for Camrest
+
+- **Repository:** https://www.repository.cam.ac.uk/handle/1810/260970
+- **Paper:** https://aclanthology.org/D16-1233/
+- **Leaderboard:** None
+- **Who transforms the dataset:** Qi Zhu(zhuq96 at gmail dot com)
+
+### Dataset Summary
+
+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.
+
+- **How to get the transformed data from original data:** 
+  - Run `python preprocess.py` in the current directory. Need `../../camrest/` as the original data.
+- **Main changes of the transformation:**
+  - Add dialogue act annotation according to the state change. This step was done by ConvLab-2 and we use the processed dialog acts here.
+  - Rename `pricerange` to `price range`
+  - Add character level span annotation for non-categorical slots.
+- **Annotations:**
+  - user goal, dialogue acts, state.
+
+### Supported Tasks and Leaderboards
+
+NLU, DST, Policy, NLG, E2E, User simulator
+
+### 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      | 406       | 3342       | 8.23    | 10.6       | 1           | 100                   | 100                  | 100                          | 99.83                           |
+| validation | 135       | 1076       | 7.97    | 11.26      | 1           | 100                   | 100                  | 100                          | 100                             |
+| test       | 135       | 1070       | 7.93    | 11.01      | 1           | 100                   | 100                  | 100                          | 100                             |
+| all        | 676       | 5488       | 8.12    | 10.81      | 1           | 100                   | 100                  | 100                          | 99.9                            |
+
+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{wen-etal-2016-conditional,
+    title = "Conditional Generation and Snapshot Learning in Neural Dialogue Systems",
+    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",
+    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
+    month = nov,
+    year = "2016",
+    address = "Austin, Texas",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/D16-1233",
+    doi = "10.18653/v1/D16-1233",
+    pages = "2153--2162",
+}
+```
+
+### Licensing Information
+
+[**CC BY 4.0**](https://creativecommons.org/licenses/by/4.0/)