| --- |
| annotations_creators: |
| - machine-generated |
| language_creators: |
| - crowdsourced |
| - machine-generated |
| language: |
| - en |
| license: |
| - apache-2.0 |
| multilinguality: |
| - monolingual |
| size_categories: |
| - 10K<n<100K |
| source_datasets: |
| - original |
| task_categories: |
| - text-generation |
| - fill-mask |
| - token-classification |
| - text-classification |
| task_ids: |
| - dialogue-modeling |
| - multi-class-classification |
| - parsing |
| paperswithcode_id: multiwoz |
| pretty_name: Multi-domain Wizard-of-Oz |
| dataset_info: |
| - config_name: v2.2 |
| features: |
| - name: dialogue_id |
| dtype: string |
| - name: services |
| sequence: string |
| - name: turns |
| sequence: |
| - name: turn_id |
| dtype: string |
| - name: speaker |
| dtype: |
| class_label: |
| names: |
| '0': USER |
| '1': SYSTEM |
| - name: utterance |
| dtype: string |
| - name: frames |
| sequence: |
| - name: service |
| dtype: string |
| - name: state |
| struct: |
| - name: active_intent |
| dtype: string |
| - name: requested_slots |
| sequence: string |
| - name: slots_values |
| sequence: |
| - name: slots_values_name |
| dtype: string |
| - name: slots_values_list |
| sequence: string |
| - name: slots |
| sequence: |
| - name: slot |
| dtype: string |
| - name: value |
| dtype: string |
| - name: start |
| dtype: int32 |
| - name: exclusive_end |
| dtype: int32 |
| - name: copy_from |
| dtype: string |
| - name: copy_from_value |
| sequence: string |
| - name: dialogue_acts |
| struct: |
| - name: dialog_act |
| sequence: |
| - name: act_type |
| dtype: string |
| - name: act_slots |
| sequence: |
| - name: slot_name |
| dtype: string |
| - name: slot_value |
| dtype: string |
| - name: span_info |
| sequence: |
| - name: act_type |
| dtype: string |
| - name: act_slot_name |
| dtype: string |
| - name: act_slot_value |
| dtype: string |
| - name: span_start |
| dtype: int32 |
| - name: span_end |
| dtype: int32 |
| splits: |
| - name: train |
| num_bytes: 68222649 |
| num_examples: 8437 |
| - name: validation |
| num_bytes: 8990945 |
| num_examples: 1000 |
| - name: test |
| num_bytes: 9027095 |
| num_examples: 1000 |
| download_size: 276592909 |
| dataset_size: 86240689 |
| - config_name: v2.2_active_only |
| features: |
| - name: dialogue_id |
| dtype: string |
| - name: services |
| sequence: string |
| - name: turns |
| sequence: |
| - name: turn_id |
| dtype: string |
| - name: speaker |
| dtype: |
| class_label: |
| names: |
| '0': USER |
| '1': SYSTEM |
| - name: utterance |
| dtype: string |
| - name: frames |
| sequence: |
| - name: service |
| dtype: string |
| - name: state |
| struct: |
| - name: active_intent |
| dtype: string |
| - name: requested_slots |
| sequence: string |
| - name: slots_values |
| sequence: |
| - name: slots_values_name |
| dtype: string |
| - name: slots_values_list |
| sequence: string |
| - name: slots |
| sequence: |
| - name: slot |
| dtype: string |
| - name: value |
| dtype: string |
| - name: start |
| dtype: int32 |
| - name: exclusive_end |
| dtype: int32 |
| - name: copy_from |
| dtype: string |
| - name: copy_from_value |
| sequence: string |
| - name: dialogue_acts |
| struct: |
| - name: dialog_act |
| sequence: |
| - name: act_type |
| dtype: string |
| - name: act_slots |
| sequence: |
| - name: slot_name |
| dtype: string |
| - name: slot_value |
| dtype: string |
| - name: span_info |
| sequence: |
| - name: act_type |
| dtype: string |
| - name: act_slot_name |
| dtype: string |
| - name: act_slot_value |
| dtype: string |
| - name: span_start |
| dtype: int32 |
| - name: span_end |
| dtype: int32 |
| splits: |
| - name: train |
| num_bytes: 40937577 |
| num_examples: 8437 |
| - name: validation |
| num_bytes: 5377939 |
| num_examples: 1000 |
| - name: test |
| num_bytes: 5410819 |
| num_examples: 1000 |
| download_size: 276592909 |
| dataset_size: 51726335 |
| --- |
| |
| # Dataset Card for MultiWOZ |
|
|
| ## Table of Contents |
| - [Dataset Description](#dataset-description) |
| - [Dataset Summary](#dataset-summary) |
| - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) |
| - [Languages](#languages) |
| - [Dataset Structure](#dataset-structure) |
| - [Data Instances](#data-instances) |
| - [Data Fields](#data-fields) |
| - [Data Splits](#data-splits) |
| - [Dataset Creation](#dataset-creation) |
| - [Curation Rationale](#curation-rationale) |
| - [Source Data](#source-data) |
| - [Annotations](#annotations) |
| - [Personal and Sensitive Information](#personal-and-sensitive-information) |
| - [Considerations for Using the Data](#considerations-for-using-the-data) |
| - [Social Impact of Dataset](#social-impact-of-dataset) |
| - [Discussion of Biases](#discussion-of-biases) |
| - [Other Known Limitations](#other-known-limitations) |
| - [Additional Information](#additional-information) |
| - [Dataset Curators](#dataset-curators) |
| - [Licensing Information](#licensing-information) |
| - [Citation Information](#citation-information) |
| - [Contributions](#contributions) |
|
|
| ## Dataset Description |
|
|
| - **Repository:** [MultiWOZ 2.2 github repository](https://github.com/budzianowski/multiwoz/tree/master/data/MultiWOZ_2.2) |
| - **Paper:** [MultiWOZ v2](https://arxiv.org/abs/1810.00278), and [MultiWOZ v2.2](https://www.aclweb.org/anthology/2020.nlp4convai-1.13.pdf) |
| - **Point of Contact:** [Paweł Budzianowski](pfb30@cam.ac.uk) |
|
|
| ### Dataset Summary |
|
|
| Multi-Domain Wizard-of-Oz dataset (MultiWOZ), a fully-labeled collection of human-human written conversations spanning over multiple domains and topics. |
| MultiWOZ 2.1 (Eric et al., 2019) identified and fixed many erroneous annotations and user utterances in the original version, resulting in an |
| improved version of the dataset. MultiWOZ 2.2 is a yet another improved version of this dataset, which identifies and fixes dialogue state annotation errors |
| across 17.3% of the utterances on top of MultiWOZ 2.1 and redefines the ontology by disallowing vocabularies of slots with a large number of possible values |
| (e.g., restaurant name, time of booking) and introducing standardized slot span annotations for these slots. |
|
|
| ### Supported Tasks and Leaderboards |
|
|
| This dataset supports a range of task. |
| - **Generative dialogue modeling** or `dialogue-modeling`: the text of the dialogues can be used to train a sequence model on the utterances. Performance on this task is typically evaluated with delexicalized-[BLEU](https://huggingface.co/metrics/bleu), inform rate and request success. |
| - **Intent state tracking**, a `multi-class-classification` task: predict the belief state of the user side of the conversation, performance is measured by [F1](https://huggingface.co/metrics/f1). |
| - **Dialog act prediction**, a `parsing` task: parse an utterance into the corresponding dialog acts for the system to use. [F1](https://huggingface.co/metrics/f1) is typically reported. |
|
|
| ### Languages |
|
|
| The text in the dataset is in English (`en`). |
|
|
| ## Dataset Structure |
|
|
| ### Data Instances |
|
|
| A data instance is a full multi-turn dialogue between a `USER` and a `SYSTEM`. Each turn has a single utterance, e.g.: |
| ``` |
| ['What fun places can I visit in the East?', |
| 'We have five spots which include boating, museums and entertainment. Any preferences that you have?'] |
| ``` |
| The utterances of the `USER` are also annotated with frames denoting their intent and believe state: |
| ``` |
| [{'service': ['attraction'], |
| 'slots': [{'copy_from': [], |
| 'copy_from_value': [], |
| 'exclusive_end': [], |
| 'slot': [], |
| 'start': [], |
| 'value': []}], |
| 'state': [{'active_intent': 'find_attraction', |
| 'requested_slots': [], |
| 'slots_values': {'slots_values_list': [['east']], |
| 'slots_values_name': ['attraction-area']}}]}, |
| {'service': [], 'slots': [], 'state': []}] |
| ``` |
| Finally, each of the utterances is annotated with dialog acts which provide a structured representation of what the `USER` or `SYSTEM` is inquiring or giving information about. |
| ``` |
| [{'dialog_act': {'act_slots': [{'slot_name': ['east'], |
| 'slot_value': ['area']}], |
| 'act_type': ['Attraction-Inform']}, |
| 'span_info': {'act_slot_name': ['area'], |
| 'act_slot_value': ['east'], |
| 'act_type': ['Attraction-Inform'], |
| 'span_end': [39], |
| 'span_start': [35]}}, |
| {'dialog_act': {'act_slots': [{'slot_name': ['none'], 'slot_value': ['none']}, |
| {'slot_name': ['boating', 'museums', 'entertainment', 'five'], |
| 'slot_value': ['type', 'type', 'type', 'choice']}], |
| 'act_type': ['Attraction-Select', 'Attraction-Inform']}, |
| 'span_info': {'act_slot_name': ['type', 'type', 'type', 'choice'], |
| 'act_slot_value': ['boating', 'museums', 'entertainment', 'five'], |
| 'act_type': ['Attraction-Inform', |
| 'Attraction-Inform', |
| 'Attraction-Inform', |
| 'Attraction-Inform'], |
| 'span_end': [40, 49, 67, 12], |
| 'span_start': [33, 42, 54, 8]}}] |
| ``` |
|
|
| ### Data Fields |
|
|
| Each dialogue instance has the following fields: |
| - `dialogue_id`: a unique ID identifying the dialog. The MUL and PMUL names refer to strictly multi domain dialogues (at least 2 main domains are involved) while the SNG, SSNG and WOZ names refer to single domain dialogues with potentially sub-domains like booking. |
| - `services`: a list of services mentioned in the dialog, such as `train` or `hospitals`. |
| - `turns`: the sequence of utterances with their annotations, including: |
| - `turn_id`: a turn identifier, unique per dialog. |
| - `speaker`: either the `USER` or `SYSTEM`. |
| - `utterance`: the text of the utterance. |
| - `dialogue_acts`: The structured parse of the utterance into dialog acts in the system's grammar |
| - `act_type`: Such as e.g. `Attraction-Inform` to seek or provide information about an `attraction` |
| - `act_slots`: provide more details about the action |
| - `span_info`: maps these `act_slots` to the `utterance` text. |
| - `frames`: only for `USER` utterances, track the user's belief state, i.e. a structured representation of what they are trying to achieve in the fialog. This decomposes into: |
| - `service`: the service they are interested in |
| - `state`: their belief state including their `active_intent` and further information expressed in `requested_slots` |
| - `slots`: a mapping of the `requested_slots` to where they are mentioned in the text. It takes one of two forms, detailed next: |
| The first type are span annotations that identify the location where slot values have been mentioned in the utterances for non-categorical slots. These span annotations are represented as follows: |
| ``` |
| { |
| "slots": [ |
| { |
| "slot": String of slot name. |
| "start": Int denoting the index of the starting character in the utterance corresponding to the slot value. |
| "exclusive_end": Int denoting the index of the character just after the last character corresponding to the slot value in the utterance. In python, utterance[start:exclusive_end] gives the slot value. |
| "value": String of value. It equals to utterance[start:exclusive_end], where utterance is the current utterance in string. |
| } |
| ] |
| } |
| ``` |
| There are also some non-categorical slots whose values are carried over from another slot in the dialogue state. Their values don"t explicitly appear in the utterances. For example, a user utterance can be "I also need a taxi from the restaurant to the hotel.", in which the state values of "taxi-departure" and "taxi-destination" are respectively carried over from that of "restaurant-name" and "hotel-name". For these slots, instead of annotating them as spans, a "copy from" annotation identifies the slot it copies the value from. This annotation is formatted as follows, |
| ``` |
| { |
| "slots": [ |
| { |
| "slot": Slot name string. |
| "copy_from": The slot to copy from. |
| "value": A list of slot values being . It corresponds to the state values of the "copy_from" slot. |
| } |
| ] |
| } |
| ``` |
|
|
| ### Data Splits |
|
|
| The dataset is split into a `train`, `validation`, and `test` split with the following sizes: |
|
|
| | | train | validation | test | |
| |---------------------|------:|-----------:|-----:| |
| | Number of dialogues | 8438 | 1000 | 1000 | |
| | Number of turns | 42190 | 5000 | 5000 | |
|
|
| ## 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 |
|
|
| The initial dataset (Versions 1.0 and 2.0) was created by a team of researchers from the [Cambridge Dialogue Systems Group](https://mi.eng.cam.ac.uk/research/dialogue/corpora/). Version 2.1 was developed on top of v2.0 by a team from Amazon, and v2.2 was developed by a team of Google researchers. |
|
|
| ### Licensing Information |
|
|
| The dataset is released under the Apache License 2.0. |
|
|
| ### Citation Information |
|
|
| You can cite the following for the various versions of MultiWOZ: |
|
|
| Version 1.0 |
| ``` |
| @inproceedings{ramadan2018large, |
| title={Large-Scale Multi-Domain Belief Tracking with Knowledge Sharing}, |
| author={Ramadan, Osman and Budzianowski, Pawe{\l} and Gasic, Milica}, |
| booktitle={Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics}, |
| volume={2}, |
| pages={432--437}, |
| year={2018} |
| } |
| ``` |
|
|
| Version 2.0 |
| ``` |
| @inproceedings{budzianowski2018large, |
| Author = {Budzianowski, Pawe{\l} and Wen, Tsung-Hsien and Tseng, Bo-Hsiang and Casanueva, I{\~n}igo and Ultes Stefan and Ramadan Osman and Ga{\v{s}}i\'c, Milica}, |
| title={MultiWOZ - A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modelling}, |
| booktitle={Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP)}, |
| year={2018} |
| } |
| ``` |
|
|
| Version 2.1 |
| ``` |
| @article{eric2019multiwoz, |
| title={MultiWOZ 2.1: Multi-Domain Dialogue State Corrections and State Tracking Baselines}, |
| author={Eric, Mihail and Goel, Rahul and Paul, Shachi and Sethi, Abhishek and Agarwal, Sanchit and Gao, Shuyag and Hakkani-Tur, Dilek}, |
| journal={arXiv preprint arXiv:1907.01669}, |
| year={2019} |
| } |
| ``` |
|
|
| Version 2.2 |
| ``` |
| @inproceedings{zang2020multiwoz, |
| title={MultiWOZ 2.2: A Dialogue Dataset with Additional Annotation Corrections and State Tracking Baselines}, |
| author={Zang, Xiaoxue and Rastogi, Abhinav and Sunkara, Srinivas and Gupta, Raghav and Zhang, Jianguo and Chen, Jindong}, |
| booktitle={Proceedings of the 2nd Workshop on Natural Language Processing for Conversational AI, ACL 2020}, |
| pages={109--117}, |
| year={2020} |
| } |
| ``` |
|
|
| ### Contributions |
|
|
| Thanks to [@yjernite](https://github.com/yjernite) for adding this dataset. |
