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banking77

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Text Classification
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Sub-tasks:
intent-classification
multi-class-classification
Languages:
English
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Duplicate from gtfintechlab/banking77

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Co-authored-by: Yang <[email protected]>

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+ ---
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+ annotations_creators:
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+ - expert-generated
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+ extended:
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+ - original
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+ language_creators:
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+ - expert-generated
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+ language:
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+ - en
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+ license:
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+ - cc-by-4.0
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+ multilinguality:
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+ - monolingual
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+ size_categories:
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+ - 10K<n<100K
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+ source_datasets:
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+ - original
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+ task_categories:
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+ - text-classification
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+ task_ids:
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+ - intent-classification
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+ - multi-class-classification
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+ paperswithcode_id: null
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+ pretty_name: BANKING77
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+ ---
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+
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+ # Dataset Card for BANKING77
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+
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+ ## Table of Contents
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+ - [Dataset Description](#dataset-description)
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+ - [Dataset Summary](#dataset-summary)
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+ - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
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+ - [Languages](#languages)
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+ - [Dataset Structure](#dataset-structure)
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+ - [Data Instances](#data-instances)
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+ - [Data Fields](#data-fields)
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+ - [Data Splits](#data-splits)
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+ - [Dataset Creation](#dataset-creation)
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+ - [Curation Rationale](#curation-rationale)
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+ - [Source Data](#source-data)
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+ - [Annotations](#annotations)
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+ - [Personal and Sensitive Information](#personal-and-sensitive-information)
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+ - [Considerations for Using the Data](#considerations-for-using-the-data)
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+ - [Social Impact of Dataset](#social-impact-of-dataset)
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+ - [Discussion of Biases](#discussion-of-biases)
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+ - [Other Known Limitations](#other-known-limitations)
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+ - [Additional Information](#additional-information)
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+ - [Dataset Curators](#dataset-curators)
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+ - [Licensing Information](#licensing-information)
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+ - [Citation Information](#citation-information)
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+ - [Contributions](#contributions)
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+
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+ ## Dataset Description
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+
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+ - **Homepage:** [Github](https://github.com/PolyAI-LDN/task-specific-datasets)
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+ - **Repository:** [Github](https://github.com/PolyAI-LDN/task-specific-datasets)
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+ - **Paper:** [ArXiv](https://arxiv.org/abs/2003.04807)
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+ - **Leaderboard:**
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+ - **Point of Contact:**
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+
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+ ### Dataset Summary
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+
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+ Dataset composed of online banking queries annotated with their corresponding intents.
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+
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+ BANKING77 dataset provides a very fine-grained set of intents in a banking domain.
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+ It comprises 13,083 customer service queries labeled with 77 intents.
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+ It focuses on fine-grained single-domain intent detection.
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+
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+ ### Supported Tasks and Leaderboards
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+
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+ Intent classification, intent detection
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+
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+ ### Languages
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+
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+ English
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+
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+ ## Dataset Structure
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+
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+ ### Data Instances
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+
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+ An example of 'train' looks as follows:
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+ ```
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+ {
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+ 'label': 11, # integer label corresponding to "card_arrival" intent
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+ 'text': 'I am still waiting on my card?'
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+ }
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+ ```
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+
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+ ### Data Fields
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+
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+ - `text`: a string feature.
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+ - `label`: One of classification labels (0-76) corresponding to unique intents.
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+
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+ Intent names are mapped to `label` in the following way:
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+
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+ | label | intent (category) |
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+ |---:|:-------------------------------------------------|
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+ | 0 | activate_my_card |
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+ | 1 | age_limit |
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+ | 2 | apple_pay_or_google_pay |
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+ | 3 | atm_support |
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+ | 4 | automatic_top_up |
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+ | 5 | balance_not_updated_after_bank_transfer |
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+ | 6 | balance_not_updated_after_cheque_or_cash_deposit |
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+ | 7 | beneficiary_not_allowed |
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+ | 8 | cancel_transfer |
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+ | 9 | card_about_to_expire |
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+ | 10 | card_acceptance |
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+ | 11 | card_arrival |
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+ | 12 | card_delivery_estimate |
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+ | 13 | card_linking |
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+ | 14 | card_not_working |
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+ | 15 | card_payment_fee_charged |
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+ | 16 | card_payment_not_recognised |
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+ | 17 | card_payment_wrong_exchange_rate |
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+ | 18 | card_swallowed |
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+ | 19 | cash_withdrawal_charge |
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+ | 20 | cash_withdrawal_not_recognised |
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+ | 21 | change_pin |
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+ | 22 | compromised_card |
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+ | 23 | contactless_not_working |
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+ | 24 | country_support |
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+ | 25 | declined_card_payment |
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+ | 26 | declined_cash_withdrawal |
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+ | 27 | declined_transfer |
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+ | 28 | direct_debit_payment_not_recognised |
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+ | 29 | disposable_card_limits |
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+ | 30 | edit_personal_details |
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+ | 31 | exchange_charge |
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+ | 32 | exchange_rate |
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+ | 33 | exchange_via_app |
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+ | 34 | extra_charge_on_statement |
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+ | 35 | failed_transfer |
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+ | 36 | fiat_currency_support |
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+ | 37 | get_disposable_virtual_card |
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+ | 38 | get_physical_card |
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+ | 39 | getting_spare_card |
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+ | 40 | getting_virtual_card |
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+ | 41 | lost_or_stolen_card |
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+ | 42 | lost_or_stolen_phone |
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+ | 43 | order_physical_card |
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+ | 44 | passcode_forgotten |
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+ | 45 | pending_card_payment |
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+ | 46 | pending_cash_withdrawal |
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+ | 47 | pending_top_up |
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+ | 48 | pending_transfer |
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+ | 49 | pin_blocked |
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+ | 50 | receiving_money |
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+ | 51 | Refund_not_showing_up |
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+ | 52 | request_refund |
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+ | 53 | reverted_card_payment? |
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+ | 54 | supported_cards_and_currencies |
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+ | 55 | terminate_account |
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+ | 56 | top_up_by_bank_transfer_charge |
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+ | 57 | top_up_by_card_charge |
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+ | 58 | top_up_by_cash_or_cheque |
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+ | 59 | top_up_failed |
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+ | 60 | top_up_limits |
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+ | 61 | top_up_reverted |
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+ | 62 | topping_up_by_card |
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+ | 63 | transaction_charged_twice |
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+ | 64 | transfer_fee_charged |
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+ | 65 | transfer_into_account |
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+ | 66 | transfer_not_received_by_recipient |
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+ | 67 | transfer_timing |
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+ | 68 | unable_to_verify_identity |
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+ | 69 | verify_my_identity |
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+ | 70 | verify_source_of_funds |
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+ | 71 | verify_top_up |
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+ | 72 | virtual_card_not_working |
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+ | 73 | visa_or_mastercard |
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+ | 74 | why_verify_identity |
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+ | 75 | wrong_amount_of_cash_received |
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+ | 76 | wrong_exchange_rate_for_cash_withdrawal |
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+
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+ ### Data Splits
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+
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+ | Dataset statistics | Train | Test |
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+ | --- | --- | --- |
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+ | Number of examples | 10 003 | 3 080 |
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+ | Average character length | 59.5 | 54.2 |
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+ | Number of intents | 77 | 77 |
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+ | Number of domains | 1 | 1 |
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+
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+ ## Dataset Creation
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+
187
+ ### Curation Rationale
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+
189
+ 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](https://github.com/xliuhw/NLU-Evaluation-Data) and [CLINC150](https://github.com/clinc/oos-eval)), 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.
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+
191
+ ### Source Data
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+
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+ #### Initial Data Collection and Normalization
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+
195
+ [More Information Needed]
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+
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+ #### Who are the source language producers?
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+
199
+ [More Information Needed]
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+
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+ ### Annotations
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+
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+ #### Annotation process
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+
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+ The dataset does not contain any additional annotations.
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+
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+ #### Who are the annotators?
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+
209
+ [N/A]
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+
211
+ ### Personal and Sensitive Information
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+
213
+ [N/A]
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+
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+ ## Considerations for Using the Data
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+
217
+ ### Social Impact of Dataset
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+
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+ The purpose of this dataset it to help develop better intent detection systems.
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+
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+ Any comprehensive intent detection evaluation should involve both coarser-grained multi-domain datasets and a fine-grained single-domain dataset such as BANKING77.
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+
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+ ### Discussion of Biases
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+
225
+ [More Information Needed]
226
+
227
+ ### Other Known Limitations
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+
229
+ [More Information Needed]
230
+
231
+ ## Additional Information
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+
233
+ ### Dataset Curators
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+
235
+ [PolyAI](https://github.com/PolyAI-LDN)
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+
237
+ ### Licensing Information
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+
239
+ Creative Commons Attribution 4.0 International
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+
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+ ### Citation Information
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+
243
+ ```
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+ @inproceedings{Casanueva2020,
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+ author = {I{\~{n}}igo Casanueva and Tadas Temcinas and Daniela Gerz and Matthew Henderson and Ivan Vulic},
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+ title = {Efficient Intent Detection with Dual Sentence Encoders},
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+ year = {2020},
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+ month = {mar},
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+ note = {Data available at https://github.com/PolyAI-LDN/task-specific-datasets},
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+ url = {https://arxiv.org/abs/2003.04807},
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+ booktitle = {Proceedings of the 2nd Workshop on NLP for ConvAI - ACL 2020}
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+ }
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+ ```
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+
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+ ### Contributions
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+
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+ Thanks to [@dkajtoch](https://github.com/dkajtoch) for adding this dataset.
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