metadata

language:
  - en
license:
  - cc-by-sa-4.0
multilinguality:
  - monolingual
pretty_name: SGD-X v3
size_categories:
  - 10K<n<100K
task_categories:
  - conversational

Dataset Card for SGD-X v3

Repository: https://github.com/google-research-datasets/dstc8-schema-guided-dialogue/tree/master/sgd_x
Paper: https://arxiv.org/pdf/2110.06800.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('sgd3')
ontology = load_ontology('sgd3')
database = load_database('sgd3')

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.

The SGD-X dataset consists of 5 linguistic variants of every schema in the original SGD dataset. Linguistic variants were written by hundreds of paid crowd-workers. In the SGD-X directory, v1 represents the variant closest to the original schemas and v5 the farthest in terms of linguistic distance. To evaluate model performance on SGD-X schemas, dialogues must be converted using the script generate_sgdx_dialogues.py.

How to get the transformed data from original data:
- Download dstc8-schema-guided-dialogue-master.zip.
- Modified sgd_x/generate_sgdx_dialogues.py as https://github.com/google-research-datasets/dstc8-schema-guided-dialogue/issues/57
- Run python -m sgd_x.generate_sgdx_dialogues under dstc8-schema-guided-dialogue-master dir which need tensorflow installed.
- 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_13', 'Buses_13', 'Buses_23', 'Calendar_13', 'Events_13', 'Events_23', 'Flights_13', 'Flights_23', 'Homes_13', 'Hotels_13', 'Hotels_23', 'Hotels_33', 'Media_13', 'Movies_13', 'Music_13', 'Music_23', 'RentalCars_13', 'RentalCars_23', 'Restaurants_13', 'RideSharing_13', 'RideSharing_23', 'Services_13', 'Services_23', 'Services_33', 'Travel_13', 'Weather_13', 'Alarm_13', 'Banks_23', 'Flights_33', 'Hotels_43', 'Media_23', 'Movies_23', 'Restaurants_23', 'Services_43', 'Buses_33', 'Events_33', 'Flights_43', 'Homes_23', 'Media_33', 'Messaging_13', 'Movies_33', 'Music_33', 'Payment_13', 'RentalCars_33', 'Trains_13']

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{lee2022sgd,
  title={SGD-X: A Benchmark for Robust Generalization in Schema-Guided Dialogue Systems},
  author={Lee, Harrison and Gupta, Raghav and Rastogi, Abhinav and Cao, Yuan and Zhang, Bin and Wu, Yonghui},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={36},
  number={10},
  pages={10938--10946},
  year={2022}
}

Licensing Information

CC BY-SA 4.0