| ## 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. | |