Datasets:
Tasks:
Text Classification
Modalities:
Text
Formats:
parquet
Sub-tasks:
intent-classification
Languages:
English
Size:
< 1K
ArXiv:
License:
| 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-description) | |
| - [Dataset Summary](#dataset-summary) | |
| - [Supported Tasks](#supported-tasks-and-leaderboards) | |
| - [Languages](#languages) | |
| - [Dataset Structure](#dataset-structure) | |
| - [Data Instances](#data-instances) | |
| - [Data Fields](#data-instances) | |
| - [Data Splits](#data-instances) | |
| - [Dataset Creation](#dataset-creation) | |
| - [Curation Rationale](#curation-rationale) | |
| - [Source Data](#source-data) | |
| - [Annotations](#annotations) | |
| - [Personal and Sensitive Information](#personal-and-sensitive-information) | |
| - [Considerations for Using the Data](#considerations-for-using-the-data) | |
| - [Social Impact of Dataset](#social-impact-of-dataset) | |
| - [Discussion of Biases](#discussion-of-biases) | |
| - [Other Known Limitations](#other-known-limitations) | |
| - [Additional Information](#additional-information) | |
| - [Dataset Curators](#dataset-curators) | |
| - [Licensing Information](#licensing-information) | |
| - [Citation Information](#citation-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 | |