Dataset Preview Go to dataset viewer
text (string)scenario (string)label (class label)
wake me up at five am this week
alarm
2 (alarm_set)
wake me up at nine am on friday
alarm
2 (alarm_set)
set an alarm for two hours from now
alarm
2 (alarm_set)
quiet
audio
4 (audio_volume_mute)
stop
audio
4 (audio_volume_mute)
pause for ten seconds
audio
4 (audio_volume_mute)
pink is all we need
iot
31 (iot_hue_lightchange)
make the lighting bit more warm here
iot
31 (iot_hue_lightchange)
please set the lighting suitable for reading
iot
31 (iot_hue_lightchange)
turn the lights off please
iot
33 (iot_hue_lightoff)
time to sleep
iot
33 (iot_hue_lightoff)
and the darkness has fallen
iot
34 (iot_hue_lighton)
turn off the light in the bathroom
iot
33 (iot_hue_lightoff)
dim the lights in the hall
iot
32 (iot_hue_lightdim)
turn the lights off in the bedroom
iot
33 (iot_hue_lightoff)
set lights to twenty percent
iot
31 (iot_hue_lightchange)
dim the lights in the kitchen
iot
32 (iot_hue_lightdim)
make a room darker
iot
32 (iot_hue_lightdim)
clean the flat
iot
29 (iot_cleaning)
it's dirty here make some noise
iot
29 (iot_cleaning)
vacuum the house
iot
29 (iot_cleaning)
cleaning is good dust is so bad do now your magic clean my carpet
iot
29 (iot_cleaning)
hoover the hallway
iot
29 (iot_cleaning)
hoover the carpets around
iot
29 (iot_cleaning)
check when the show starts
calendar
7 (calendar_query)
i want to listen arijit singh song once again
play
48 (play_music)
i want to play that music one again
play
48 (play_music)
check my car is ready
general
27 (general_quirky)
check my laptop is working
general
27 (general_quirky)
is the brightness of my screen running low
general
27 (general_quirky)
i need to have location services on can you check
general
27 (general_quirky)
check the status of my power usage
general
27 (general_quirky)
i want the status on my screen brightness
general
27 (general_quirky)
give me the status on my available memory
general
27 (general_quirky)
i am not tired i am actually happy
general
27 (general_quirky)
what's up
general
23 (general_greet)
what's the time in australia
datetime
13 (datetime_query)
tell me the time in moscow
datetime
13 (datetime_query)
tell me the time in g. m. t. plus five
datetime
12 (datetime_convert)
most rated delivery options for chinese food
takeaway
62 (takeaway_query)
i want some curry to go any recommendations
takeaway
62 (takeaway_query)
find my thai takeaways around grassmarket
takeaway
62 (takeaway_query)
cancel my seven am alarm
alarm
1 (alarm_remove)
remove the alarm set for ten pm
alarm
1 (alarm_remove)
stop seven am alarm
alarm
1 (alarm_remove)
what alarms i have set
alarm
0 (alarm_query)
please list active alarms
alarm
0 (alarm_query)
tell me about my alarms
alarm
0 (alarm_query)
whats new in london
news
45 (news_query)
tell me latest bbc news
news
45 (news_query)
whats happening in football today
news
45 (news_query)
please play yesterday from beatles
play
48 (play_music)
i'd like to hear queen's barcelona
play
48 (play_music)
play me barcelona by queen
play
48 (play_music)
i like rock music
music
42 (music_likeness)
my favourite music band is queen
music
42 (music_likeness)
i like senatra songs
music
42 (music_likeness)
start playing music from favourites
play
48 (play_music)
please play my best music
play
48 (play_music)
play something from recent playlist
play
48 (play_music)
what's the band is playing now
music
43 (music_query)
who's current music's author
music
43 (music_query)
what's that the album is current music from
music
43 (music_query)
i'm really enjoying this song
music
42 (music_likeness)
the song you are playing is amazing
music
42 (music_likeness)
this is one of the best songs for me
music
42 (music_likeness)
set lights brightness higher
iot
35 (iot_hue_lightup)
make lights brightener
iot
35 (iot_hue_lightup)
please raise the lights to max
iot
35 (iot_hue_lightup)
hey start vacuum cleaner robot
iot
29 (iot_cleaning)
enable cleaner robot
iot
29 (iot_cleaning)
turn cleaner robot on
iot
29 (iot_cleaning)
tell me today's date
datetime
13 (datetime_query)
what date is it today
datetime
13 (datetime_query)
what's the date is currently
datetime
13 (datetime_query)
please order some sushi for dinner
takeaway
61 (takeaway_order)
hey i'd like you to order burger
takeaway
61 (takeaway_order)
could you order sushi for tonight dinner
takeaway
61 (takeaway_order)
whats with my dinner order
takeaway
62 (takeaway_query)
can i order takeaway dinner from byron's
takeaway
61 (takeaway_order)
does byron's supports takeaways
takeaway
62 (takeaway_query)
set an alarm for twelve
alarm
2 (alarm_set)
set an alarm forty minutes from now
alarm
2 (alarm_set)
set alarm for eight every weekday
alarm
2 (alarm_set)
is it raining
weather
67 (weather_query)
is it going to rain
weather
67 (weather_query)
is it currently snowing
weather
67 (weather_query)
whats the weeks forecast
weather
67 (weather_query)
whats this weeks weather
weather
67 (weather_query)
tell me the weather this week
weather
67 (weather_query)
tell me bbc news
news
45 (news_query)
whats the news on bbc news
news
45 (news_query)
what is the bbc's latest news
news
45 (news_query)
play hurts like heaven
play
48 (play_music)
play a song i like
play
48 (play_music)
put on cannibal queen
play
48 (play_music)
play daft punk
play
48 (play_music)
put on some coldplay
play
48 (play_music)
play some david bowie
play
48 (play_music)
loop this track
music
44 (music_settings)
End of preview (truncated to 100 rows)

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.

Update on GitHub