Datasets:
annotations_creators:
- expert-generated
extended:
- original
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: BANKING77
Dataset Card for BANKING77
Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
Dataset Description
Dataset Summary
Dataset composed of online banking queries annotated with their corresponding intents.
BANKING77 dataset provides a very fine-grained set of intents in a banking domain. It comprises 13,083 customer service queries labeled with 77 intents. It focuses on fine-grained single-domain intent detection.
Supported Tasks and Leaderboards
Intent classification, intent detection
Languages
English
Dataset Structure
Data Instances
An example of 'train' looks as follows:
{
'label': 11, # integer label corresponding to "card_arrival" intent
'text': 'I am still waiting on my card?'
}
Data Fields
text: a string feature.label: One of classification labels (0-76) corresponding to unique intents.
Intent names are mapped to label in the following way:
| label | intent (category) |
|---|---|
| 0 | activate_my_card |
| 1 | age_limit |
| 2 | apple_pay_or_google_pay |
| 3 | atm_support |
| 4 | automatic_top_up |
| 5 | balance_not_updated_after_bank_transfer |
| 6 | balance_not_updated_after_cheque_or_cash_deposit |
| 7 | beneficiary_not_allowed |
| 8 | cancel_transfer |
| 9 | card_about_to_expire |
| 10 | card_acceptance |
| 11 | card_arrival |
| 12 | card_delivery_estimate |
| 13 | card_linking |
| 14 | card_not_working |
| 15 | card_payment_fee_charged |
| 16 | card_payment_not_recognised |
| 17 | card_payment_wrong_exchange_rate |
| 18 | card_swallowed |
| 19 | cash_withdrawal_charge |
| 20 | cash_withdrawal_not_recognised |
| 21 | change_pin |
| 22 | compromised_card |
| 23 | contactless_not_working |
| 24 | country_support |
| 25 | declined_card_payment |
| 26 | declined_cash_withdrawal |
| 27 | declined_transfer |
| 28 | direct_debit_payment_not_recognised |
| 29 | disposable_card_limits |
| 30 | edit_personal_details |
| 31 | exchange_charge |
| 32 | exchange_rate |
| 33 | exchange_via_app |
| 34 | extra_charge_on_statement |
| 35 | failed_transfer |
| 36 | fiat_currency_support |
| 37 | get_disposable_virtual_card |
| 38 | get_physical_card |
| 39 | getting_spare_card |
| 40 | getting_virtual_card |
| 41 | lost_or_stolen_card |
| 42 | lost_or_stolen_phone |
| 43 | order_physical_card |
| 44 | passcode_forgotten |
| 45 | pending_card_payment |
| 46 | pending_cash_withdrawal |
| 47 | pending_top_up |
| 48 | pending_transfer |
| 49 | pin_blocked |
| 50 | receiving_money |
| 51 | Refund_not_showing_up |
| 52 | request_refund |
| 53 | reverted_card_payment? |
| 54 | supported_cards_and_currencies |
| 55 | terminate_account |
| 56 | top_up_by_bank_transfer_charge |
| 57 | top_up_by_card_charge |
| 58 | top_up_by_cash_or_cheque |
| 59 | top_up_failed |
| 60 | top_up_limits |
| 61 | top_up_reverted |
| 62 | topping_up_by_card |
| 63 | transaction_charged_twice |
| 64 | transfer_fee_charged |
| 65 | transfer_into_account |
| 66 | transfer_not_received_by_recipient |
| 67 | transfer_timing |
| 68 | unable_to_verify_identity |
| 69 | verify_my_identity |
| 70 | verify_source_of_funds |
| 71 | verify_top_up |
| 72 | virtual_card_not_working |
| 73 | visa_or_mastercard |
| 74 | why_verify_identity |
| 75 | wrong_amount_of_cash_received |
| 76 | wrong_exchange_rate_for_cash_withdrawal |
Data Splits
| Dataset statistics | Train | Test |
|---|---|---|
| Number of examples | 10 003 | 3 080 |
| Average character length | 59.5 | 54.2 |
| Number of intents | 77 | 77 |
| Number of domains | 1 | 1 |
Dataset Creation
Curation Rationale
Previous intent detection datasets such as Web Apps, Ask Ubuntu, the Chatbot Corpus or SNIPS are limited to small number of classes (<10), which oversimplifies the intent detection task and does not emulate the true environment of commercial systems. Although there exist large scale multi-domain datasets (HWU64 and CLINC150), the examples per each domain may not sufficiently capture the full complexity of each domain as encountered "in the wild". This dataset tries to fill the gap and provides a very fine-grained set of intents in a single-domain i.e. banking. Its focus on fine-grained single-domain intent detection makes it complementary to the other two multi-domain datasets.
Source Data
Initial Data Collection and Normalization
[More Information Needed]
Who are the source language producers?
[More Information Needed]
Annotations
Annotation process
The dataset does not contain any additional annotations.
Who are the annotators?
[N/A]
Personal and Sensitive Information
[N/A]
Considerations for Using the Data
Social Impact of Dataset
The purpose of this dataset it to help develop better intent detection systems.
Any comprehensive intent detection evaluation should involve both coarser-grained multi-domain datasets and a fine-grained single-domain dataset such as BANKING77.
Discussion of Biases
[More Information Needed]
Other Known Limitations
[More Information Needed]
Additional Information
Dataset Curators
Licensing Information
Creative Commons Attribution 4.0 International
Citation Information
@inproceedings{Casanueva2020,
author = {I{\~{n}}igo Casanueva and Tadas Temcinas and Daniela Gerz and Matthew Henderson and Ivan Vulic},
title = {Efficient Intent Detection with Dual Sentence Encoders},
year = {2020},
month = {mar},
note = {Data available at https://github.com/PolyAI-LDN/task-specific-datasets},
url = {https://arxiv.org/abs/2003.04807},
booktitle = {Proceedings of the 2nd Workshop on NLP for ConvAI - ACL 2020}
}
Contributions
Thanks to @dkajtoch for adding this dataset.