Datasets:
annotations_creators:
- expert-generated
language_creators:
- expert-generated
languages:
- en
licenses:
- cc0-1-0
multilinguality:
- monolingual
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- intent-classification
Dataset Card for Snips Built In Intents
Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
Dataset Description
- Homepage: https://github.com/sonos/nlu-benchmark/tree/master/2016-12-built-in-intents
- Repository: https://github.com/sonos/nlu-benchmark/tree/master/2016-12-built-in-intents
- Paper: https://arxiv.org/abs/1805.10190
- Point of Contact: The Snips team has joined Sonos in November 2019. These open datasets remain available and their access is now managed by the Sonos Voice Experience Team. Please email sve-research@sonos.com with any question.
Dataset Summary
Snips' built in intents dataset was initially used to compare different voice assistants and released as a public dataset hosted at https://github.com/sonos/nlu-benchmark in folder 2016-12-built-in-intents. The dataset contains 328 utterances over 10 intent classes. A related Medium post is https://medium.com/snips-ai/benchmarking-natural-language-understanding-systems-d35be6ce568d.
Supported Tasks and Leaderboards
There are no related shared tasks that we are aware of.
Languages
English
Dataset Structure
Data Instances
The dataset contains 328 utterances over 10 intent classes. Each sample looks like:
{'label': 8, 'text': 'Transit directions to Barcelona Pizza.'}
Data Fields
text: The text utterance expressing some user intent.label: The intent label of the piece of text utterance.
Data Splits
The source data is not split.
Dataset Creation
Curation Rationale
The dataset was originally created to compare the performance of a number of voice assistants. However, the labelled utterances are useful for developing and benchmarking text chatbots as well.
Source Data
Initial Data Collection and Normalization
It is not clear how the data was collected. From the Medium post: The benchmark relies on a set of 328 queries built by the business team at Snips, and kept secret from data scientists and engineers throughout the development of the solution.
Who are the source language producers?
Originally prepared by snips.ai. The Snips team has since joined Sonos in November 2019. These open datasets remain available and their access is now managed by the Sonos Voice Experience Team. Please email sve-research@sonos.com with any question.
Annotations
Annotation process
It is not clear how the data was collected. From the Medium post: The benchmark relies on a set of 328 queries built by the business team at Snips, and kept secret from data scientists and engineers throughout the development of the solution.
Who are the annotators?
[More Information Needed]
Personal and Sensitive Information
[More Information Needed]
Considerations for Using the Data
Social Impact of Dataset
[More Information Needed]
Discussion of Biases
[More Information Needed]
Other Known Limitations
[More Information Needed]
Additional Information
Dataset Curators
Originally prepared by snips.ai. The Snips team has since joined Sonos in November 2019. These open datasets remain available and their access is now managed by the Sonos Voice Experience Team. Please email sve-research@sonos.com with any question.
Licensing Information
The source data is licensed under Creative Commons Zero v1.0 Universal.
Citation Information
Any publication based on these datasets must include a full citation to the following paper in which the results were published by the Snips Team:
Coucke A. et al., "Snips Voice Platform: an embedded Spoken Language Understanding system for private-by-design voice interfaces." CoRR 2018, https://arxiv.org/abs/1805.10190