add camrest dataset in unified data format

data/unified_datasets/camrest/README.md

@@ -1,24 +1,61 @@
-# README
+# Dataset Card for Camrest
 
-## Features
+- **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)
 
-- Annotations: dialogue act, character-level span for non-categorical slots.
+### Dataset Summary
 
-Statistics: 
+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.
 
-|       | \# dialogues | \# utterances | avg. turns | avg. tokens | \# domains |
-| ----- | ------------ | ------------- | ---------- | ----------- | ---------- |
-| train | 406         | 2936         | 7.23     | 11.36       | 1          |
-| dev | 135         | 941         | 6.97      | 11.99       | 1          |
-| train | 135         | 935         | 6.93       | 11.87       | 1          |
+- **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.
 
-## Main changes
+### Supported Tasks and Leaderboards
 
-- domain is set to **restaurant**
-- ignore some rare pair
-- 3 values are not found in original utterances
-- **dontcare** values in non-categorical slots are calculated in `evaluate.py` so `da_match` in evaluation is lower than actual number.
+NLU, DST, Policy, NLG, E2E, User simulator
 
-## Original data
+### Languages
 
-camrest used in convlab2, included in `data/` path
+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/)