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--- |
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annotations_creators: |
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- machine-generated |
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language_creators: |
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- crowdsourced |
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- machine-generated |
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languages: |
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- en |
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licenses: |
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- apache-2-0 |
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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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- sequence-modeling |
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- structure-prediction |
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- text-classification |
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task_ids: |
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- dialogue-modeling |
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- multi-class-classification |
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- parsing |
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--- |
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# Dataset Card for MultiWOZ |
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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](#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-instances) |
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- [Data Splits](#data-instances) |
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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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## Dataset Description |
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- **Repository:** [MultiWOZ 2.2 github repository](https://github.com/budzianowski/multiwoz/tree/master/data/MultiWOZ_2.2) |
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- **Paper:** [MultiWOZ v2](https://arxiv.org/abs/1810.00278), and [MultiWOZ v2.2](https://www.aclweb.org/anthology/2020.nlp4convai-1.13.pdf) |
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- **Point of Contact:** [Paweł Budzianowski](pfb30@cam.ac.uk) |
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### Dataset Summary |
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Multi-Domain Wizard-of-Oz dataset (MultiWOZ), a fully-labeled collection of human-human written conversations spanning over multiple domains and topics. |
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MultiWOZ 2.1 (Eric et al., 2019) identified and fixed many erroneous annotations and user utterances in the original version, resulting in an |
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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 |
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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 |
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(e.g., restaurant name, time of booking) and introducing standardized slot span annotations for these slots. |
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### Supported Tasks and Leaderboards |
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This dataset supports a range of task. |
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- **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. |
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- **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). |
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- **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. |
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### Languages |
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The text in the dataset is in English (`en`). |
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## Dataset Structure |
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### Data Instances |
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A data instance is a full multi-turn dialogue between a `USER` and a `SYSTEM`. Each turn has a single utterance, e.g.: |
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``` |
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['What fun places can I visit in the East?', |
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'We have five spots which include boating, museums and entertainment. Any preferences that you have?'] |
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``` |
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The utterances of the `USER` are also annotated with frames denoting their intent and believe state: |
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``` |
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[{'service': ['attraction'], |
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'slots': [{'copy_from': [], |
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'copy_from_value': [], |
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'exclusive_end': [], |
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'slot': [], |
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'start': [], |
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'value': []}], |
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'state': [{'active_intent': 'find_attraction', |
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'requested_slots': [], |
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'slots_values': {'slots_values_list': [['east']], |
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'slots_values_name': ['attraction-area']}}]}, |
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{'service': [], 'slots': [], 'state': []}] |
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``` |
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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. |
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``` |
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[{'dialog_act': {'act_slots': [{'slot_name': ['east'], |
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'slot_value': ['area']}], |
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'act_type': ['Attraction-Inform']}, |
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'span_info': {'act_slot_name': ['area'], |
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'act_slot_value': ['east'], |
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'act_type': ['Attraction-Inform'], |
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'span_end': [39], |
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'span_start': [35]}}, |
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{'dialog_act': {'act_slots': [{'slot_name': ['none'], 'slot_value': ['none']}, |
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{'slot_name': ['boating', 'museums', 'entertainment', 'five'], |
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'slot_value': ['type', 'type', 'type', 'choice']}], |
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'act_type': ['Attraction-Select', 'Attraction-Inform']}, |
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'span_info': {'act_slot_name': ['type', 'type', 'type', 'choice'], |
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'act_slot_value': ['boating', 'museums', 'entertainment', 'five'], |
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'act_type': ['Attraction-Inform', |
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'Attraction-Inform', |
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'Attraction-Inform', |
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'Attraction-Inform'], |
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'span_end': [40, 49, 67, 12], |
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'span_start': [33, 42, 54, 8]}}] |
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``` |
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### Data Fields |
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Each dialogue instance has the following fields: |
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- `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. |
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- `services`: a list of services mentioned in the dialog, such as `train` or `hospitals`. |
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- `turns`: the sequence of utterances with their annotations, including: |
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- `turn_id`: a turn identifier, unique per dialog. |
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- `speaker`: either the `USER` or `SYSTEM`. |
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- `utterance`: the text of the utterance. |
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- `dialogue_acts`: The structured parse of the utterance into dialog acts in the system's grammar |
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- `act_type`: Such as e.g. `Attraction-Inform` to seek or provide information about an `attraction` |
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- `act_slots`: provide more details about the action |
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- `span_info`: maps these `act_slots` to the `utterance` text. |
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- `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: |
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- `service`: the service they are interested in |
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- `state`: their belief state including their `active_intent` and further information expressed in `requested_slots` |
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- `slots`: a mapping of the `requested_slots` to where they are mentioned in the text. It takes one of two forms, detailed next: |
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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: |
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``` |
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{ |
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"slots": [ |
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{ |
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"slot": String of slot name. |
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"start": Int denoting the index of the starting character in the utterance corresponding to the slot value. |
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"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. |
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"value": String of value. It equals to utterance[start:exclusive_end], where utterance is the current utterance in string. |
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} |
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] |
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} |
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``` |
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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, |
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``` |
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{ |
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"slots": [ |
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{ |
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"slot": Slot name string. |
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"copy_from": The slot to copy from. |
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"value": A list of slot values being . It corresponds to the state values of the "copy_from" slot. |
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} |
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] |
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} |
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``` |
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### Data Splits |
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The dataset is split into a `train`, `validation`, and `test` split with the following sizes: |
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| | Tain | Valid | Test | |
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| ----- | ------ | ----- | ---- | |
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| Number of dialogues | 8438 | 1000 | 1000 | |
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| Number of turns | 42190 | 5000 | 5000 | |
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## Dataset Creation |
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### Curation Rationale |
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[More Information Needed] |
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### Source Data |
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#### Initial Data Collection and Normalization |
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[More Information Needed] |
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#### Who are the source language producers? |
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[More Information Needed] |
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### Annotations |
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#### Annotation process |
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[More Information Needed] |
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#### Who are the annotators? |
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[More Information Needed] |
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### Personal and Sensitive Information |
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[More Information Needed] |
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## Considerations for Using the Data |
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### Social Impact of Dataset |
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[More Information Needed] |
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### Discussion of Biases |
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[More Information Needed] |
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### Other Known Limitations |
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[More Information Needed] |
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## Additional Information |
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### Dataset Curators |
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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. |
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### Licensing Information |
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The dataset is released under the Apache License 2.0. |
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### Citation Information |
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You can cite the following for the various versions of MultiWOZ: |
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Version 1.0 |
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``` |
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@inproceedings{ramadan2018large, |
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title={Large-Scale Multi-Domain Belief Tracking with Knowledge Sharing}, |
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author={Ramadan, Osman and Budzianowski, Pawe{\l} and Gasic, Milica}, |
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booktitle={Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics}, |
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volume={2}, |
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pages={432--437}, |
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year={2018} |
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} |
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``` |
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Version 2.0 |
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``` |
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@inproceedings{budzianowski2018large, |
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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}, |
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title={MultiWOZ - A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modelling}, |
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booktitle={Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP)}, |
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year={2018} |
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} |
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``` |
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Version 2.1 |
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``` |
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@article{eric2019multiwoz, |
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title={MultiWOZ 2.1: Multi-Domain Dialogue State Corrections and State Tracking Baselines}, |
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author={Eric, Mihail and Goel, Rahul and Paul, Shachi and Sethi, Abhishek and Agarwal, Sanchit and Gao, Shuyag and Hakkani-Tur, Dilek}, |
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journal={arXiv preprint arXiv:1907.01669}, |
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year={2019} |
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} |
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``` |
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Version 2.2 |
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``` |
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@inproceedings{zang2020multiwoz, |
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title={MultiWOZ 2.2: A Dialogue Dataset with Additional Annotation Corrections and State Tracking Baselines}, |
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author={Zang, Xiaoxue and Rastogi, Abhinav and Sunkara, Srinivas and Gupta, Raghav and Zhang, Jianguo and Chen, Jindong}, |
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booktitle={Proceedings of the 2nd Workshop on Natural Language Processing for Conversational AI, ACL 2020}, |
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pages={109--117}, |
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year={2020} |
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} |
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``` |
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