Datasets:
text
stringlengths 1
365
| scenario
stringclasses 18
values | label
class label 68
classes |
---|---|---|
wake me up at five am this week | alarm | 2alarm_set
|
wake me up at nine am on friday | alarm | 2alarm_set
|
set an alarm for two hours from now | alarm | 2alarm_set
|
quiet | audio | 4audio_volume_mute
|
stop | audio | 4audio_volume_mute
|
pause for ten seconds | audio | 4audio_volume_mute
|
pink is all we need | iot | 31iot_hue_lightchange
|
make the lighting bit more warm here | iot | 31iot_hue_lightchange
|
please set the lighting suitable for reading | iot | 31iot_hue_lightchange
|
turn the lights off please | iot | 33iot_hue_lightoff
|
time to sleep | iot | 33iot_hue_lightoff
|
and the darkness has fallen | iot | 34iot_hue_lighton
|
turn off the light in the bathroom | iot | 33iot_hue_lightoff
|
dim the lights in the hall | iot | 32iot_hue_lightdim
|
turn the lights off in the bedroom | iot | 33iot_hue_lightoff
|
set lights to twenty percent | iot | 31iot_hue_lightchange
|
dim the lights in the kitchen | iot | 32iot_hue_lightdim
|
make a room darker | iot | 32iot_hue_lightdim
|
clean the flat | iot | 29iot_cleaning
|
it's dirty here make some noise | iot | 29iot_cleaning
|
vacuum the house | iot | 29iot_cleaning
|
cleaning is good dust is so bad do now your magic clean my carpet | iot | 29iot_cleaning
|
hoover the hallway | iot | 29iot_cleaning
|
hoover the carpets around | iot | 29iot_cleaning
|
check when the show starts | calendar | 7calendar_query
|
i want to listen arijit singh song once again | play | 48play_music
|
i want to play that music one again | play | 48play_music
|
check my car is ready | general | 27general_quirky
|
check my laptop is working | general | 27general_quirky
|
is the brightness of my screen running low | general | 27general_quirky
|
i need to have location services on can you check | general | 27general_quirky
|
check the status of my power usage | general | 27general_quirky
|
i want the status on my screen brightness | general | 27general_quirky
|
give me the status on my available memory | general | 27general_quirky
|
i am not tired i am actually happy | general | 27general_quirky
|
what's up | general | 23general_greet
|
what's the time in australia | datetime | 13datetime_query
|
tell me the time in moscow | datetime | 13datetime_query
|
tell me the time in g. m. t. plus five | datetime | 12datetime_convert
|
most rated delivery options for chinese food | takeaway | 62takeaway_query
|
i want some curry to go any recommendations | takeaway | 62takeaway_query
|
find my thai takeaways around grassmarket | takeaway | 62takeaway_query
|
cancel my seven am alarm | alarm | 1alarm_remove
|
remove the alarm set for ten pm | alarm | 1alarm_remove
|
stop seven am alarm | alarm | 1alarm_remove
|
what alarms i have set | alarm | 0alarm_query
|
please list active alarms | alarm | 0alarm_query
|
tell me about my alarms | alarm | 0alarm_query
|
whats new in london | news | 45news_query
|
tell me latest bbc news | news | 45news_query
|
whats happening in football today | news | 45news_query
|
please play yesterday from beatles | play | 48play_music
|
i'd like to hear queen's barcelona | play | 48play_music
|
play me barcelona by queen | play | 48play_music
|
i like rock music | music | 42music_likeness
|
my favourite music band is queen | music | 42music_likeness
|
i like senatra songs | music | 42music_likeness
|
start playing music from favourites | play | 48play_music
|
please play my best music | play | 48play_music
|
play something from recent playlist | play | 48play_music
|
what's the band is playing now | music | 43music_query
|
who's current music's author | music | 43music_query
|
what's that the album is current music from | music | 43music_query
|
i'm really enjoying this song | music | 42music_likeness
|
the song you are playing is amazing | music | 42music_likeness
|
this is one of the best songs for me | music | 42music_likeness
|
set lights brightness higher | iot | 35iot_hue_lightup
|
make lights brightener | iot | 35iot_hue_lightup
|
please raise the lights to max | iot | 35iot_hue_lightup
|
hey start vacuum cleaner robot | iot | 29iot_cleaning
|
enable cleaner robot | iot | 29iot_cleaning
|
turn cleaner robot on | iot | 29iot_cleaning
|
tell me today's date | datetime | 13datetime_query
|
what date is it today | datetime | 13datetime_query
|
what's the date is currently | datetime | 13datetime_query
|
please order some sushi for dinner | takeaway | 61takeaway_order
|
hey i'd like you to order burger | takeaway | 61takeaway_order
|
could you order sushi for tonight dinner | takeaway | 61takeaway_order
|
whats with my dinner order | takeaway | 62takeaway_query
|
can i order takeaway dinner from byron's | takeaway | 61takeaway_order
|
does byron's supports takeaways | takeaway | 62takeaway_query
|
set an alarm for twelve | alarm | 2alarm_set
|
set an alarm forty minutes from now | alarm | 2alarm_set
|
set alarm for eight every weekday | alarm | 2alarm_set
|
is it raining | weather | 67weather_query
|
is it going to rain | weather | 67weather_query
|
is it currently snowing | weather | 67weather_query
|
whats the weeks forecast | weather | 67weather_query
|
whats this weeks weather | weather | 67weather_query
|
tell me the weather this week | weather | 67weather_query
|
tell me bbc news | news | 45news_query
|
whats the news on bbc news | news | 45news_query
|
what is the bbc's latest news | news | 45news_query
|
play hurts like heaven | play | 48play_music
|
play a song i like | play | 48play_music
|
put on cannibal queen | play | 48play_music
|
play daft punk | play | 48play_music
|
put on some coldplay | play | 48play_music
|
play some david bowie | play | 48play_music
|
loop this track | music | 44music_settings
|
Dataset Card for NLU Evaluation Data
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.
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 for adding this dataset.
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