metadata

language:
  - en
license:
  - cc-by-sa-4.0
multilinguality:
  - monolingual
pretty_name: SGD
size_categories:
  - 10K<n<100K
task_categories:
  - conversational

Dataset Card for Schema-Guided Dialogue

Repository: https://github.com/google-research-datasets/dstc8-schema-guided-dialogue
Paper: https://arxiv.org/pdf/1909.05855.pdf
Leaderboard: None
Who transforms the dataset: Qi Zhu(zhuq96 at gmail dot com)

To use this dataset, you need to install ConvLab-3 platform first. Then you can load the dataset via:

from convlab.util import load_dataset, load_ontology, load_database

dataset = load_dataset('sgd')
ontology = load_ontology('sgd')
database = load_database('sgd')

For more usage please refer to here.

Dataset Summary

The Schema-Guided Dialogue (SGD) dataset consists of over 20k annotated multi-domain, task-oriented conversations between a human and a virtual assistant. These conversations involve interactions with services and APIs spanning 20 domains, such as banks, events, media, calendar, travel, and weather. For most of these domains, the dataset contains multiple different APIs, many of which have overlapping functionalities but different interfaces, which reflects common real-world scenarios. The wide range of available annotations can be used for intent prediction, slot filling, dialogue state tracking, policy imitation learning, language generation, and user simulation learning, among other tasks for developing large-scale virtual assistants. Additionally, the dataset contains unseen domains and services in the evaluation set to quantify the performance in zero-shot or few-shot settings.

How to get the transformed data from original data:
- Download dstc8-schema-guided-dialogue-master.zip.
- Run python preprocess.py in the current directory.
Main changes of the transformation:
- Lower case original act as intent.
- Add count slot for each domain, non-categorical, find span by text matching.
- Categorize dialogue acts according to the intent.
- Concatenate multiple values using |.
- Retain active_intent, requested_slots, service_call.
Annotations:
- dialogue acts, state, db_results, service_call, active_intent, requested_slots.

Supported Tasks and Leaderboards

NLU, DST, Policy, NLG, E2E

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	16142	329964	20.44	9.75	1.84	100	-	100	100
validation	2482	48726	19.63	9.66	1.84	100	-	100	100
test	4201	84594	20.14	10.4	2.02	100	-	100	100
all	22825	463284	20.3	9.86	1.87	100	-	100	100

45 domains: ['Banks_1', 'Buses_1', 'Buses_2', 'Calendar_1', 'Events_1', 'Events_2', 'Flights_1', 'Flights_2', 'Homes_1', 'Hotels_1', 'Hotels_2', 'Hotels_3', 'Media_1', 'Movies_1', 'Music_1', 'Music_2', 'RentalCars_1', 'RentalCars_2', 'Restaurants_1', 'RideSharing_1', 'RideSharing_2', 'Services_1', 'Services_2', 'Services_3', 'Travel_1', 'Weather_1', 'Alarm_1', 'Banks_2', 'Flights_3', 'Hotels_4', 'Media_2', 'Movies_2', 'Restaurants_2', 'Services_4', 'Buses_3', 'Events_3', 'Flights_4', 'Homes_2', 'Media_3', 'Messaging_1', 'Movies_3', 'Music_3', 'Payment_1', 'RentalCars_3', 'Trains_1']

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

@article{rastogi2019towards,
  title={Towards Scalable Multi-domain Conversational Agents: The Schema-Guided Dialogue Dataset},
  author={Rastogi, Abhinav and Zang, Xiaoxue and Sunkara, Srinivas and Gupta, Raghav and Khaitan, Pranav},
  journal={arXiv preprint arXiv:1909.05855},
  year={2019}
}

Licensing Information

CC BY-SA 4.0