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## Simulator Generated Dataset (sim-GEN)
This directory contains an expanded set of dialogues generated via dialogue
self-play between a user simulator and a system agent, as follows:
- The dialogues collected using the M2M framework for the movie ticket booking
task (sim-M) are used as a seed set to form a crowd-sourced corpus of
natural language utterances for the user and the system agents.
- Subsequently, many more dialogue outlines are generated using self-play
between the simulated user and system agent.
- The dialogue outlines are converted to natural language dialogues by
replacing each dialogue act in the outline with an utterance sampled from
the set of crowd-sourced utterances collected with M2M.
In this manner, we can generate an arbitrarily large number of dialogue outlines
and convert them automatically to natural language dialogues without any
additional crowd-sourcing step. Although the diversity of natural language in
the dataset does not increase, the number of unique dialogue states present in
the dataset will increase since a larger variety of dialogue outlines will be
available in the expanded dataset.
This dataset was used for experiments reported in [this
paper](https://arxiv.org/abs/1804.06512). Please cite the paper if you use or
discuss sim-GEN in your work:
```shell
@article{liu2018dialogue,
title={Dialogue Learning with Human Teaching and Feedback in End-to-End Trainable Task-Oriented Dialogue Systems},
author={Liu, Bing and Tur, Gokhan and Hakkani-Tur, Dilek and Shah, Pararth and Heck, Larry},
journal={NAACL},
year={2018}
}
```
## Data format
The data splits are made available as a .zip file containing dialogues in JSON
format. Each dialogue object contains the following fields:
* **dialogue\_id** - *string* unique identifier for each dialogue.
* **turns** - *list* of turn objects:
* **system\_acts** - *list* of system dialogue acts for this system turn:
* **name** - *string* system act name
* **slot\_values** - *optional dictionary* mapping slot names to
values
* **system\_utterance** - *string* natural language utterance
corresponding to the system acts for this turn
* **user\_utterance** - *string* natural language user utterance following
the system utterance in this turn
* **dialogue\_state** - *dictionary* ground truth slot-value mapping after
the user utterance
* **database\_state** - database results based on current dialogue state:
* **scores** - *list* of scores, between 0.0 and 1.0, of top 5
database results. 1.0 means matches all constraints and 0.0 means no
match
* **has\_more\_results** - *boolean* whether backend has more matching
results
* **has\_no\_results** - *boolean* whether backend has no matching
results
An additional file **db.json** is provided which contains the set of values for
each slot.
Note: The date values in the dataset are normalized as the constants,
"base_date_plus_X", for X from 0 to 6. X=0 corresponds to the current date (i.e.
'today'), X=1 is 'tomorrow', etc. This is done to allow handling of relative
references to dates (e.g. 'this weekend', 'next Wednesday', etc). The parsing of
such phrases should be done as a separate pre-processing step.
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