Datasets:
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-3.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- intent-classification
paperswithcode_id: clinc150
pretty_name: CLINC150
dataset_info:
- config_name: imbalanced
features:
- name: text
dtype: string
- name: intent
dtype:
class_label:
names:
'0': restaurant_reviews
'1': nutrition_info
'2': account_blocked
'3': oil_change_how
'4': time
'5': weather
'6': redeem_rewards
'7': interest_rate
'8': gas_type
'9': accept_reservations
'10': smart_home
'11': user_name
'12': report_lost_card
'13': repeat
'14': whisper_mode
'15': what_are_your_hobbies
'16': order
'17': jump_start
'18': schedule_meeting
'19': meeting_schedule
'20': freeze_account
'21': what_song
'22': meaning_of_life
'23': restaurant_reservation
'24': traffic
'25': make_call
'26': text
'27': bill_balance
'28': improve_credit_score
'29': change_language
'30': 'no'
'31': measurement_conversion
'32': timer
'33': flip_coin
'34': do_you_have_pets
'35': balance
'36': tell_joke
'37': last_maintenance
'38': exchange_rate
'39': uber
'40': car_rental
'41': credit_limit
'42': oos
'43': shopping_list
'44': expiration_date
'45': routing
'46': meal_suggestion
'47': tire_change
'48': todo_list
'49': card_declined
'50': rewards_balance
'51': change_accent
'52': vaccines
'53': reminder_update
'54': food_last
'55': change_ai_name
'56': bill_due
'57': who_do_you_work_for
'58': share_location
'59': international_visa
'60': calendar
'61': translate
'62': carry_on
'63': book_flight
'64': insurance_change
'65': todo_list_update
'66': timezone
'67': cancel_reservation
'68': transactions
'69': credit_score
'70': report_fraud
'71': spending_history
'72': directions
'73': spelling
'74': insurance
'75': what_is_your_name
'76': reminder
'77': where_are_you_from
'78': distance
'79': payday
'80': flight_status
'81': find_phone
'82': greeting
'83': alarm
'84': order_status
'85': confirm_reservation
'86': cook_time
'87': damaged_card
'88': reset_settings
'89': pin_change
'90': replacement_card_duration
'91': new_card
'92': roll_dice
'93': income
'94': taxes
'95': date
'96': who_made_you
'97': pto_request
'98': tire_pressure
'99': how_old_are_you
'100': rollover_401k
'101': pto_request_status
'102': how_busy
'103': application_status
'104': recipe
'105': calendar_update
'106': play_music
'107': 'yes'
'108': direct_deposit
'109': credit_limit_change
'110': gas
'111': pay_bill
'112': ingredients_list
'113': lost_luggage
'114': goodbye
'115': what_can_i_ask_you
'116': book_hotel
'117': are_you_a_bot
'118': next_song
'119': change_speed
'120': plug_type
'121': maybe
'122': w2
'123': oil_change_when
'124': thank_you
'125': shopping_list_update
'126': pto_balance
'127': order_checks
'128': travel_alert
'129': fun_fact
'130': sync_device
'131': schedule_maintenance
'132': apr
'133': transfer
'134': ingredient_substitution
'135': calories
'136': current_location
'137': international_fees
'138': calculator
'139': definition
'140': next_holiday
'141': update_playlist
'142': mpg
'143': min_payment
'144': change_user_name
'145': restaurant_suggestion
'146': travel_notification
'147': cancel
'148': pto_used
'149': travel_suggestion
'150': change_volume
splits:
- name: train
num_bytes: 546901
num_examples: 10625
- name: validation
num_bytes: 160298
num_examples: 3100
- name: test
num_bytes: 286966
num_examples: 5500
download_size: 246833
dataset_size: 994165
- config_name: plus
features:
- name: text
dtype: string
- name: intent
dtype:
class_label:
names:
'0': restaurant_reviews
'1': nutrition_info
'2': account_blocked
'3': oil_change_how
'4': time
'5': weather
'6': redeem_rewards
'7': interest_rate
'8': gas_type
'9': accept_reservations
'10': smart_home
'11': user_name
'12': report_lost_card
'13': repeat
'14': whisper_mode
'15': what_are_your_hobbies
'16': order
'17': jump_start
'18': schedule_meeting
'19': meeting_schedule
'20': freeze_account
'21': what_song
'22': meaning_of_life
'23': restaurant_reservation
'24': traffic
'25': make_call
'26': text
'27': bill_balance
'28': improve_credit_score
'29': change_language
'30': 'no'
'31': measurement_conversion
'32': timer
'33': flip_coin
'34': do_you_have_pets
'35': balance
'36': tell_joke
'37': last_maintenance
'38': exchange_rate
'39': uber
'40': car_rental
'41': credit_limit
'42': oos
'43': shopping_list
'44': expiration_date
'45': routing
'46': meal_suggestion
'47': tire_change
'48': todo_list
'49': card_declined
'50': rewards_balance
'51': change_accent
'52': vaccines
'53': reminder_update
'54': food_last
'55': change_ai_name
'56': bill_due
'57': who_do_you_work_for
'58': share_location
'59': international_visa
'60': calendar
'61': translate
'62': carry_on
'63': book_flight
'64': insurance_change
'65': todo_list_update
'66': timezone
'67': cancel_reservation
'68': transactions
'69': credit_score
'70': report_fraud
'71': spending_history
'72': directions
'73': spelling
'74': insurance
'75': what_is_your_name
'76': reminder
'77': where_are_you_from
'78': distance
'79': payday
'80': flight_status
'81': find_phone
'82': greeting
'83': alarm
'84': order_status
'85': confirm_reservation
'86': cook_time
'87': damaged_card
'88': reset_settings
'89': pin_change
'90': replacement_card_duration
'91': new_card
'92': roll_dice
'93': income
'94': taxes
'95': date
'96': who_made_you
'97': pto_request
'98': tire_pressure
'99': how_old_are_you
'100': rollover_401k
'101': pto_request_status
'102': how_busy
'103': application_status
'104': recipe
'105': calendar_update
'106': play_music
'107': 'yes'
'108': direct_deposit
'109': credit_limit_change
'110': gas
'111': pay_bill
'112': ingredients_list
'113': lost_luggage
'114': goodbye
'115': what_can_i_ask_you
'116': book_hotel
'117': are_you_a_bot
'118': next_song
'119': change_speed
'120': plug_type
'121': maybe
'122': w2
'123': oil_change_when
'124': thank_you
'125': shopping_list_update
'126': pto_balance
'127': order_checks
'128': travel_alert
'129': fun_fact
'130': sync_device
'131': schedule_maintenance
'132': apr
'133': transfer
'134': ingredient_substitution
'135': calories
'136': current_location
'137': international_fees
'138': calculator
'139': definition
'140': next_holiday
'141': update_playlist
'142': mpg
'143': min_payment
'144': change_user_name
'145': restaurant_suggestion
'146': travel_notification
'147': cancel
'148': pto_used
'149': travel_suggestion
'150': change_volume
splits:
- name: train
num_bytes: 791247
num_examples: 15250
- name: validation
num_bytes: 160298
num_examples: 3100
- name: test
num_bytes: 286966
num_examples: 5500
download_size: 291179
dataset_size: 1238511
- config_name: small
features:
- name: text
dtype: string
- name: intent
dtype:
class_label:
names:
'0': restaurant_reviews
'1': nutrition_info
'2': account_blocked
'3': oil_change_how
'4': time
'5': weather
'6': redeem_rewards
'7': interest_rate
'8': gas_type
'9': accept_reservations
'10': smart_home
'11': user_name
'12': report_lost_card
'13': repeat
'14': whisper_mode
'15': what_are_your_hobbies
'16': order
'17': jump_start
'18': schedule_meeting
'19': meeting_schedule
'20': freeze_account
'21': what_song
'22': meaning_of_life
'23': restaurant_reservation
'24': traffic
'25': make_call
'26': text
'27': bill_balance
'28': improve_credit_score
'29': change_language
'30': 'no'
'31': measurement_conversion
'32': timer
'33': flip_coin
'34': do_you_have_pets
'35': balance
'36': tell_joke
'37': last_maintenance
'38': exchange_rate
'39': uber
'40': car_rental
'41': credit_limit
'42': oos
'43': shopping_list
'44': expiration_date
'45': routing
'46': meal_suggestion
'47': tire_change
'48': todo_list
'49': card_declined
'50': rewards_balance
'51': change_accent
'52': vaccines
'53': reminder_update
'54': food_last
'55': change_ai_name
'56': bill_due
'57': who_do_you_work_for
'58': share_location
'59': international_visa
'60': calendar
'61': translate
'62': carry_on
'63': book_flight
'64': insurance_change
'65': todo_list_update
'66': timezone
'67': cancel_reservation
'68': transactions
'69': credit_score
'70': report_fraud
'71': spending_history
'72': directions
'73': spelling
'74': insurance
'75': what_is_your_name
'76': reminder
'77': where_are_you_from
'78': distance
'79': payday
'80': flight_status
'81': find_phone
'82': greeting
'83': alarm
'84': order_status
'85': confirm_reservation
'86': cook_time
'87': damaged_card
'88': reset_settings
'89': pin_change
'90': replacement_card_duration
'91': new_card
'92': roll_dice
'93': income
'94': taxes
'95': date
'96': who_made_you
'97': pto_request
'98': tire_pressure
'99': how_old_are_you
'100': rollover_401k
'101': pto_request_status
'102': how_busy
'103': application_status
'104': recipe
'105': calendar_update
'106': play_music
'107': 'yes'
'108': direct_deposit
'109': credit_limit_change
'110': gas
'111': pay_bill
'112': ingredients_list
'113': lost_luggage
'114': goodbye
'115': what_can_i_ask_you
'116': book_hotel
'117': are_you_a_bot
'118': next_song
'119': change_speed
'120': plug_type
'121': maybe
'122': w2
'123': oil_change_when
'124': thank_you
'125': shopping_list_update
'126': pto_balance
'127': order_checks
'128': travel_alert
'129': fun_fact
'130': sync_device
'131': schedule_maintenance
'132': apr
'133': transfer
'134': ingredient_substitution
'135': calories
'136': current_location
'137': international_fees
'138': calculator
'139': definition
'140': next_holiday
'141': update_playlist
'142': mpg
'143': min_payment
'144': change_user_name
'145': restaurant_suggestion
'146': travel_notification
'147': cancel
'148': pto_used
'149': travel_suggestion
'150': change_volume
splits:
- name: train
num_bytes: 394124
num_examples: 7600
- name: validation
num_bytes: 160298
num_examples: 3100
- name: test
num_bytes: 286966
num_examples: 5500
download_size: 216522
dataset_size: 841388
Dataset Card for CLINC150
Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
Dataset Description
- Homepage: Github
- Repository: Github
- Paper: Aclweb
- Leaderboard: PapersWithCode
- Point of Contact:
Dataset Summary
Task-oriented dialog systems need to know when a query falls outside their range of supported intents, but current text classification corpora only define label sets that cover every example. We introduce a new dataset that includes queries that are out-of-scope (OOS), i.e., queries that do not fall into any of the system's supported intents. This poses a new challenge because models cannot assume that every query at inference time belongs to a system-supported intent class. Our dataset also covers 150 intent classes over 10 domains, capturing the breadth that a production task-oriented agent must handle. It offers a way of more rigorously and realistically benchmarking text classification in task-driven dialog systems.
Supported Tasks and Leaderboards
intent-classification: This dataset is for evaluating the performance of intent classification systems in the presence of "out-of-scope" queries, i.e., queries that do not fall into any of the system-supported intent classes. The dataset includes both in-scope and out-of-scope data. here.
Languages
English
Dataset Structure
Data Instances
A sample from the training set is provided below:
{
'text' : 'can you walk me through setting up direct deposits to my bank of internet savings account',
'label' : 108
}
Data Fields
- text : Textual data
- label : 150 intent classes over 10 domains, the dataset contains one label for 'out-of-scope' intent.
The Label Id to Label Name map is mentioned in the table below:
| Label Id | Label name | |--- |--- | | 0 | restaurant_reviews | | 1 | nutrition_info | | 2 | account_blocked | | 3 | oil_change_how | | 4 | time | | 5 | weather | | 6 | redeem_rewards | | 7 | interest_rate | | 8 | gas_type | | 9 | accept_reservations | | 10 | smart_home | | 11 | user_name | | 12 | report_lost_card | | 13 | repeat | | 14 | whisper_mode | | 15 | what_are_your_hobbies | | 16 | order | | 17 | jump_start | | 18 | schedule_meeting | | 19 | meeting_schedule | | 20 | freeze_account | | 21 | what_song | | 22 | meaning_of_life | | 23 | restaurant_reservation | | 24 | traffic | | 25 | make_call | | 26 | text | | 27 | bill_balance | | 28 | improve_credit_score | | 29 | change_language | | 30 | no | | 31 | measurement_conversion | | 32 | timer | | 33 | flip_coin | | 34 | do_you_have_pets | | 35 | balance | | 36 | tell_joke | | 37 | last_maintenance | | 38 | exchange_rate | | 39 | uber | | 40 | car_rental | | 41 | credit_limit | | 42 | oos | | 43 | shopping_list | | 44 | expiration_date | | 45 | routing | | 46 | meal_suggestion | | 47 | tire_change | | 48 | todo_list | | 49 | card_declined | | 50 | rewards_balance | | 51 | change_accent | | 52 | vaccines | | 53 | reminder_update | | 54 | food_last | | 55 | change_ai_name | | 56 | bill_due | | 57 | who_do_you_work_for | | 58 | share_location | | 59 | international_visa | | 60 | calendar | | 61 | translate | | 62 | carry_on | | 63 | book_flight | | 64 | insurance_change | | 65 | todo_list_update | | 66 | timezone | | 67 | cancel_reservation | | 68 | transactions | | 69 | credit_score | | 70 | report_fraud | | 71 | spending_history | | 72 | directions | | 73 | spelling | | 74 | insurance | | 75 | what_is_your_name | | 76 | reminder | | 77 | where_are_you_from | | 78 | distance | | 79 | payday | | 80 | flight_status | | 81 | find_phone | | 82 | greeting | | 83 | alarm | | 84 | order_status | | 85 | confirm_reservation | | 86 | cook_time | | 87 | damaged_card | | 88 | reset_settings | | 89 | pin_change | | 90 | replacement_card_duration | | 91 | new_card | | 92 | roll_dice | | 93 | income | | 94 | taxes | | 95 | date | | 96 | who_made_you | | 97 | pto_request | | 98 | tire_pressure | | 99 | how_old_are_you | | 100 | rollover_401k | | 101 | pto_request_status | | 102 | how_busy | | 103 | application_status | | 104 | recipe | | 105 | calendar_update | | 106 | play_music | | 107 | yes | | 108 | direct_deposit | | 109 | credit_limit_change | | 110 | gas | | 111 | pay_bill | | 112 | ingredients_list | | 113 | lost_luggage | | 114 | goodbye | | 115 | what_can_i_ask_you | | 116 | book_hotel | | 117 | are_you_a_bot | | 118 | next_song | | 119 | change_speed | | 120 | plug_type | | 121 | maybe | | 122 | w2 | | 123 | oil_change_when | | 124 | thank_you | | 125 | shopping_list_update | | 126 | pto_balance | | 127 | order_checks | | 128 | travel_alert | | 129 | fun_fact | | 130 | sync_device | | 131 | schedule_maintenance | | 132 | apr | | 133 | transfer | | 134 | ingredient_substitution | | 135 | calories | | 136 | current_location | | 137 | international_fees | | 138 | calculator | | 139 | definition | | 140 | next_holiday | | 141 | update_playlist | | 142 | mpg | | 143 | min_payment | | 144 | change_user_name | | 145 | restaurant_suggestion | | 146 | travel_notification | | 147 | cancel | | 148 | pto_used | | 149 | travel_suggestion | | 150 | change_volume |
Data Splits
The dataset comes in different subsets:
small: Small, in which there are only 50 training queries per each in-scope intentimbalanced: Imbalanced, in which intents have either 25, 50, 75, or 100 training queries.plus: OOS+, in which there are 250 out-of-scope training examples, rather than 100.
| name | train | validation | test |
|---|---|---|---|
| small | 7600 | 3100 | 5500 |
| imbalanced | 10625 | 3100 | 5500 |
| plus | 15250 | 3100 | 5500 |
Dataset Creation
Curation Rationale
[More Information Needed]
Source Data
Initial Data Collection and Normalization
[More Information Needed]
Who are the source language producers?
[More Information Needed]
Annotations
Annotation process
[More Information Needed]
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
[More Information Needed]
Licensing Information
[More Information Needed]
Citation Information
@inproceedings{larson-etal-2019-evaluation,
title = "An Evaluation Dataset for Intent Classification and Out-of-Scope Prediction",
author = "Larson, Stefan and
Mahendran, Anish and
Peper, Joseph J. and
Clarke, Christopher and
Lee, Andrew and
Hill, Parker and
Kummerfeld, Jonathan K. and
Leach, Kevin and
Laurenzano, Michael A. and
Tang, Lingjia and
Mars, Jason",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
year = "2019",
url = "https://www.aclweb.org/anthology/D19-1131"
}
Contributions
Thanks to @sumanthd17 for adding this dataset.