Datasets:

xingkunliuxtracta
/

nlu_evaluation_data

	---
	annotations_creators:
	- expert-generated
	language_creators:
	- expert-generated
	language:
	- en
	license:
	- cc-by-4.0
	multilinguality:
	- monolingual
	size_categories:
	- 10K<n<100K
	source_datasets:
	- original
	task_categories:
	- text-classification
	task_ids:
	- intent-classification
	- multi-class-classification
	paperswithcode_id: null
	pretty_name: NLU Evaluation Data
	dataset_info:
	features:
	- name: text
	dtype: string
	- name: scenario
	dtype: string
	- name: label
	dtype:
	class_label:
	names:
	0: alarm_query
	1: alarm_remove
	2: alarm_set
	3: audio_volume_down
	4: audio_volume_mute
	5: audio_volume_other
	6: audio_volume_up
	7: calendar_query
	8: calendar_remove
	9: calendar_set
	10: cooking_query
	11: cooking_recipe
	12: datetime_convert
	13: datetime_query
	14: email_addcontact
	15: email_query
	16: email_querycontact
	17: email_sendemail
	18: general_affirm
	19: general_commandstop
	20: general_confirm
	21: general_dontcare
	22: general_explain
	23: general_greet
	24: general_joke
	25: general_negate
	26: general_praise
	27: general_quirky
	28: general_repeat
	29: iot_cleaning
	30: iot_coffee
	31: iot_hue_lightchange
	32: iot_hue_lightdim
	33: iot_hue_lightoff
	34: iot_hue_lighton
	35: iot_hue_lightup
	36: iot_wemo_off
	37: iot_wemo_on
	38: lists_createoradd
	39: lists_query
	40: lists_remove
	41: music_dislikeness
	42: music_likeness
	43: music_query
	44: music_settings
	45: news_query
	46: play_audiobook
	47: play_game
	48: play_music
	49: play_podcasts
	50: play_radio
	51: qa_currency
	52: qa_definition
	53: qa_factoid
	54: qa_maths
	55: qa_stock
	56: recommendation_events
	57: recommendation_locations
	58: recommendation_movies
	59: social_post
	60: social_query
	61: takeaway_order
	62: takeaway_query
	63: transport_query
	64: transport_taxi
	65: transport_ticket
	66: transport_traffic
	67: weather_query
	splits:
	- name: train
	num_bytes: 1447941
	num_examples: 25715
	download_size: 5867439
	dataset_size: 1447941
	---

	# Dataset Card for NLU Evaluation Data

	## Table of Contents
	- [Dataset Description](#dataset-description)
	- [Dataset Summary](#dataset-summary)
	- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
	- [Languages](#languages)
	- [Dataset Structure](#dataset-structure)
	- [Data Instances](#data-instances)
	- [Data Fields](#data-fields)
	- [Data Splits](#data-splits)
	- [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)
	- [Contributions](#contributions)

	## Dataset Description

	- Homepage: [Github](https://github.com/xliuhw/NLU-Evaluation-Data)
	- Repository: [Github](https://github.com/xliuhw/NLU-Evaluation-Data)
	- Paper: [ArXiv](https://arxiv.org/abs/1903.05566)
	- Leaderboard:
	- Point of Contact: [x.liu@hw.ac.uk](mailto:x.liu@hw.ac.uk)

	### Dataset Summary

	Dataset with short utterances from conversational domain annotated with their corresponding intents and scenarios.

	It has 25 715 non-zero examples (original dataset has 25716 examples) belonging to 18 scenarios and 68 intents.
	Originally, the dataset was crowd-sourced and annotated with both intents and named entities
	in order to evaluate commercial NLU systems such as RASA, IBM's Watson, Microsoft's LUIS and Google's Dialogflow.
	This version of the dataset only includes intent annotations!

	In contrast to paper claims, released data contains 68 unique intents. This is due to the fact, that NLU systems were
	evaluated on more curated part of this dataset which only included 64 most important intents. Read more in [github issue](https://github.com/xliuhw/NLU-Evaluation-Data/issues/5).

	### Supported Tasks and Leaderboards

	Intent classification, intent detection

	### Languages

	English

	## Dataset Structure

	### Data Instances

	An example of 'train' looks as follows:
	```
	{
	'label': 2, # integer label corresponding to "alarm_set" intent
	'scenario': 'alarm',
	'text': 'wake me up at five am this week'
	}
	```

	### Data Fields

	- `text`: a string feature.
	- `label`: one of classification labels (0-67) corresponding to unique intents.
	- `scenario`: a string with one of unique scenarios (18).

	Intent names are mapped to `label` in the following way:

	\| label \| intent \|
	\|--------:\|:-------------------------\|
	\| 0 \| alarm_query \|
	\| 1 \| alarm_remove \|
	\| 2 \| alarm_set \|
	\| 3 \| audio_volume_down \|
	\| 4 \| audio_volume_mute \|
	\| 5 \| audio_volume_other \|
	\| 6 \| audio_volume_up \|
	\| 7 \| calendar_query \|
	\| 8 \| calendar_remove \|
	\| 9 \| calendar_set \|
	\| 10 \| cooking_query \|
	\| 11 \| cooking_recipe \|
	\| 12 \| datetime_convert \|
	\| 13 \| datetime_query \|
	\| 14 \| email_addcontact \|
	\| 15 \| email_query \|
	\| 16 \| email_querycontact \|
	\| 17 \| email_sendemail \|
	\| 18 \| general_affirm \|
	\| 19 \| general_commandstop \|
	\| 20 \| general_confirm \|
	\| 21 \| general_dontcare \|
	\| 22 \| general_explain \|
	\| 23 \| general_greet \|
	\| 24 \| general_joke \|
	\| 25 \| general_negate \|
	\| 26 \| general_praise \|
	\| 27 \| general_quirky \|
	\| 28 \| general_repeat \|
	\| 29 \| iot_cleaning \|
	\| 30 \| iot_coffee \|
	\| 31 \| iot_hue_lightchange \|
	\| 32 \| iot_hue_lightdim \|
	\| 33 \| iot_hue_lightoff \|
	\| 34 \| iot_hue_lighton \|
	\| 35 \| iot_hue_lightup \|
	\| 36 \| iot_wemo_off \|
	\| 37 \| iot_wemo_on \|
	\| 38 \| lists_createoradd \|
	\| 39 \| lists_query \|
	\| 40 \| lists_remove \|
	\| 41 \| music_dislikeness \|
	\| 42 \| music_likeness \|
	\| 43 \| music_query \|
	\| 44 \| music_settings \|
	\| 45 \| news_query \|
	\| 46 \| play_audiobook \|
	\| 47 \| play_game \|
	\| 48 \| play_music \|
	\| 49 \| play_podcasts \|
	\| 50 \| play_radio \|
	\| 51 \| qa_currency \|
	\| 52 \| qa_definition \|
	\| 53 \| qa_factoid \|
	\| 54 \| qa_maths \|
	\| 55 \| qa_stock \|
	\| 56 \| recommendation_events \|
	\| 57 \| recommendation_locations \|
	\| 58 \| recommendation_movies \|
	\| 59 \| social_post \|
	\| 60 \| social_query \|
	\| 61 \| takeaway_order \|
	\| 62 \| takeaway_query \|
	\| 63 \| transport_query \|
	\| 64 \| transport_taxi \|
	\| 65 \| transport_ticket \|
	\| 66 \| transport_traffic \|
	\| 67 \| weather_query \|

	### Data Splits

	\| Dataset statistics \| Train \|
	\| --- \| --- \|
	\| Number of examples \| 25 715 \|
	\| Average character length \| 34.32 \|
	\| Number of intents \| 68 \|
	\| Number of scenarios \| 18 \|

	## Dataset Creation

	### Curation Rationale

	The dataset was prepared for a wide coverage evaluation and comparison of some of the most popular NLU services.
	At that time, previous benchmarks were done with few intents and spawning limited number of domains. Here, the dataset
	is much larger and contains 68 intents from 18 scenarios, which is much larger that any previous evaluation. For more discussion see the paper.

	### Source Data

	#### Initial Data Collection and Normalization

	[More Information Needed]

	#### Who are the source language producers?

	[More Information Needed]

	### Annotations

	#### Annotation process

	> To build the NLU component we collected real user data via Amazon Mechanical Turk (AMT). We designed tasks where the Turker’s goal was to answer questions about how people would interact with the home robot, in a wide range of scenarios designed in advance, namely: alarm, audio, audiobook, calendar, cooking, datetime, email, game, general, IoT, lists, music, news, podcasts, general Q&A, radio, recommendations, social, food takeaway, transport, and weather.
	The questions put to Turkers were designed to capture the different requests within each given scenario.
	In the ‘calendar’ scenario, for example, these pre-designed intents were included: ‘set event’, ‘delete event’ and ‘query event’.
	An example question for intent ‘set event’ is: “How would you ask your PDA to schedule a meeting with someone?” for which a user’s answer example was “Schedule a chat with Adam on Thursday afternoon”.
	The Turkers would then type in their answers to these questions and select possible entities from the pre-designed suggested entities list for each of their answers.The Turkers didn’t always follow the instructions fully, e.g. for the specified ‘delete event’ Intent, an answer was: “PDA what is my next event?”; which clearly belongs to ‘query event’ Intent.
	We have manually corrected all such errors either during post-processing or the subsequent annotations.

	#### Who are the annotators?

	[More Information Needed]

	### Personal and Sensitive Information

	[More Information Needed]

	## Considerations for Using the Data

	### Social Impact of Dataset

	The purpose of this dataset it to help develop better intent detection systems.

	### Discussion of Biases

	[More Information Needed]

	### Other Known Limitations

	[More Information Needed]

	## Additional Information

	### Dataset Curators

	[More Information Needed]

	### Licensing Information

	Creative Commons Attribution 4.0 International License (CC BY 4.0)

	### Citation Information
	```
	@InProceedings{XLiu.etal:IWSDS2019,
	author = {Xingkun Liu, Arash Eshghi, Pawel Swietojanski and Verena Rieser},
	title = {Benchmarking Natural Language Understanding Services for building Conversational Agents},
	booktitle = {Proceedings of the Tenth International Workshop on Spoken Dialogue Systems Technology (IWSDS)},
	month = {April},
	year = {2019},
	address = {Ortigia, Siracusa (SR), Italy},
	publisher = {Springer},
	pages = {xxx--xxx},
	url = {http://www.xx.xx/xx/}
	}
	```
	### Contributions

	Thanks to [@dkajtoch](https://github.com/dkajtoch) for adding this dataset.