Dataset Card for BANKING77

Dataset Summary

Deprecated: Dataset "banking77" is deprecated and will be deleted. Use " PolyAI/banking77 " instead.

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

PolyAI

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.