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鈥檚 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 鈥榗alendar鈥 scenario, for example, these pre-designed intents were included: 鈥榮et event鈥, 鈥榙elete event鈥 and 鈥榪uery event鈥. An example question for intent 鈥榮et event鈥 is: 鈥淗ow would you ask your PDA to schedule a meeting with someone?鈥 for which a user鈥檚 answer example was 鈥淪chedule a chat with Adam on Thursday afternoon鈥. 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鈥檛 always follow the instructions fully, e.g. for the specified 鈥榙elete event鈥 Intent, an answer was: 鈥淧DA what is my next event?鈥; which clearly belongs to 鈥榪uery event鈥 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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