|
--- |
|
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]([email protected]) |
|
|
|
### 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. |
