Datasets:
license: other
multilinguality:
- monolingual
language:
- en
pretty_name: Dialog2Flow Training Corpus
size_categories:
- 1M<n<10M
source_datasets:
- Salesforce/dialogstudio
task_categories:
- sentence-similarity
- feature-extraction
- text2text-generation
- text-generation
tags:
- task-oriented-dialog
- task-oriented-dialogues
- dialog-flow
- dialog-modeling
- dialogue-flow
- dialogue-modeling
- conversational-ia
- dialog-acts
- slots
Dialog2Flow Training Corpus
This page hosts the dataset introduced in the paper "Dialog2Flow: Pre-training Soft-Contrastive Action-Driven Sentence Embeddings for Automatic Dialog Flow Extraction" published in the EMNLP 2024 main conference. Here we are not only making available the dataset but also each one of the 20 (standardized) task-oriented dialogue datasets used to build it.
The corpus consists of 3.4 million utterances/sentences annotated with dialog act and slot labels across 52 different domains. Domain names and dialog act labels were manually standardized across the 20 datasets.
Load Training Datasets
From this corpus, in the paper, 3 datasets were created for training the sentence encoders, one for the single target (D2F_single) training containing the subset with both dialog act and slots annotation; and other two for the joint target (DFD_joint), one containing the subset with dialog acts and another with slots only. To use them, you can use one of the following names, respectively:
"dialog-acts+slots": (utterance, action label) pairs."dialog-acts": (utterance, dialog act label) pairs."slots": (utterance, slots label) pairs.
For instance, below is one example to load the "dialog-acts+slots" dataset:
from datasets import load_dataset
dataset = load_dataset('sergioburdisso/dialog2flow-dataset', 'dialog-acts+slots', trust_remote_code=True)
print(dataset)
Output:
DatasetDict({
train: Dataset({
features: ['utterance', 'label'],
num_rows: 1577184
})
validation: Dataset({
features: ['utterance', 'label'],
num_rows: 4695
})
})
Load (Individual) Task-Oriented Dialog Datasets
We also provide access to each one of the 20 task-oriented dialogue datasets standardizing annotation and format used to build the corpus. To load each dataset we can simply use its name as given in the following table with information about License and number of dialogues in each dataset:
| Dataset Name | Train | Validation | Test | Total | License |
|---|---|---|---|---|---|
| ABCD | 8034 | 1004 | 1004 | 10042 | MIT License |
| BiTOD | 2952 | 295 | 442 | 3689 | Apache License 2.0 |
| Disambiguation | 8433 | 999 | 1000 | 10432 | MiT License |
| DSTC2-Clean | 1612 | 506 | 1117 | 3235 | GNU GENERAL PUBLIC LICENSE Version 3, 29 June 2007 |
| FRAMES | 1329 | - | 40 | 1369 | GNU GENERAL PUBLIC LICENSE Version 3, 29 June 2007 |
| GECOR | 676 | - | - | 676 | CC BY 4.0 |
| HDSA-Dialog | 8438 | 1000 | 1000 | 10438 | MIT License |
| KETOD | 4247 | 545 | 532 | 5324 | MiT License |
| MS-DC | 10000 | - | - | 10000 | MICROSOFT RESEARCH LICENSE TERMS |
| MulDoGO | 59939 | 1150 | 2319 | 63408 | Community Data License Agreement – Permissive – Version 1.0 |
| MultiWOZ_2.1 | 8434 | 999 | 1000 | 10433 | MiT License |
| MULTIWOZ2_2 | 8437 | 1000 | 1000 | 10437 | Mit License |
| SGD | 16142 | 2482 | 4201 | 22825 | CC BY-SA 4.0 |
| SimJointGEN | 100000 | 10000 | 10000 | 120000 | No license |
| SimJointMovie | 384 | 120 | 264 | 768 | No license |
| SimJointRestaurant | 1116 | 349 | 775 | 2240 | No license |
| Taskmaster1 | 6170 | 769 | 769 | 7708 | Attribution 4.0 International (CC BY 4.0) |
| Taskmaster2 | 17304 | - | - | 17304 | Creative Commons Attribution 4.0 License (CC BY 4.0) |
| Taskmaster3 | 22724 | 17019 | 17903 | 57646 | Creative Commons Attribution 4.0 License (CC BY 4.0) |
| WOZ2_0 | 600 | 200 | 400 | 1200 | Apache License 2.0 |
For instance, below is one example to load the "WOZ2_0" dataset:
from datasets import load_dataset
dataset = load_dataset('sergioburdisso/dialog2flow-dataset', 'WOZ2_0', trust_remote_code=True)
print(dataset)
Output:
DatasetDict({
test: Dataset({
features: ['dialog'],
num_rows: 400
})
train: Dataset({
features: ['dialog'],
num_rows: 600
})
validation: Dataset({
features: ['dialog'],
num_rows: 200
})
})
Note that, unlike previous datasets that contained utterance-label pairs, these individual datasets consist of only one feature "dialog" since their a collection of dialogs (not utterances). Each dialog in turn has the JSON structure described in Appendix A of the paper. For instance, let's get the first dialog of the train split:
print(dataset["train"][0]["dialog"])
Output:
[
{
"speaker":"user",
"text":"Are there any eritrean restaurants in town?",
"domains":[
"restaurant"
],
"labels":{
"dialog_acts":{
"acts":[
"inform"
],
"main_acts":[
"inform"
],
"original_acts":[
"inform"
]
},
"slots":[
"food"
],
"intents":"None"
}
},
...
{
"speaker":"system",
"text":"There is a wide variety of Chinese restaurants, do you have an area preference or a price preference to narrow it down?",
"domains":[
"restaurant"
],
"labels":{
"dialog_acts":{
"acts":[
"request"
],
"main_acts":[
"request"
],
"original_acts":[
"request"
]
},
"slots":[
"area"
],
"intents":"None"
}
},
...
]
Corpus Details
Stats
- Utterances: 3.4M
- Domains: 52
- Dialogs: 369,174
- Labels:
- Dialog acts: 18
- Slots: 524
- Actions (dialog act + slots): 3,982
Full List of Dialog Acts
List of the final 18 dialog act labels along with their proportion in the corpus:
inform (64.66%) ·
request (12.62%) ·
offer (6.62%) ·
inform_success (3.07%) ·
good_bye (2.67%) ·
agreement (2.45%) ·
thank_you (2.25%) ·
confirm (2.10%) ·
disagreement (1.60%) ·
request_more (1.06%) ·
request_alternative (0.90%) ·
recommendation (0.70%) ·
inform_failure (0.64%) ·
greeting (0.31%) ·
confirm_answer (0.18%) ·
confirm_question (0.17%) ·
request_update (0.02%) ·
request_compare (0.01%)
Full List of Domains
List of the final 52 domain names along with their proportion in the corpus:
movie (32.98%) ·
restaurant (13.48%) ·
hotel (10.15%) ·
train (4.52%) ·
flight (4.30%) ·
event (3.56%) ·
attraction (3.50%) ·
service (2.44%) ·
bus (2.28%) ·
taxi (2.21%) ·
rentalcars (2.20%) ·
travel (2.16%) ·
music (1.81%) ·
medium (1.66%) ·
ridesharing (1.30%) ·
booking (1.21%) ·
home (1.01%) ·
finance (0.79%) ·
airline (0.69%) ·
calendar (0.69%) ·
fastfood (0.68%) ·
insurance (0.61%) ·
weather (0.58%) ·
bank (0.47%) ·
hkmtr (0.36%) ·
mlb (0.35%) ·
ml (0.31%) ·
food (0.30%) ·
epl (0.30%) ·
pizza (0.25%) ·
coffee (0.24%) ·
uber (0.24%) ·
software (0.23%) ·
auto (0.21%) ·
nba (0.20%) ·
product_defect (0.17%) ·
shipping_issue (0.16%) ·
alarm (0.13%) ·
order_issue (0.13%) ·
messaging (0.13%) ·
hospital (0.11%) ·
subscription_inquiry (0.11%) ·
account_access (0.11%) ·
payment (0.10%) ·
purchase_dispute (0.10%) ·
nfl (0.09%) ·
chat (0.08%) ·
police (0.07%) ·
single_item_query (0.06%) ·
storewide_query (0.06%) ·
troubleshoot_site (0.06%) ·
manage_account (0.06%)
More details about the corpus can be found in Section 4 and Appendix A of the original paper.
Citation
If you found the paper and/or this repository useful, please consider citing our work :)
EMNLP paper: here.
@inproceedings{burdisso-etal-2024-dialog2flow,
title = "{D}ialog2{F}low: Pre-training Soft-Contrastive Action-Driven Sentence Embeddings for Automatic Dialog Flow Extraction",
author = "Burdisso, Sergio and
Madikeri, Srikanth and
Motlicek, Petr",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.emnlp-main.310",
pages = "5421--5440",
}
License
Individual datasets were originally loaded from DialogStudio and therefore, this project follows their licensing structure. For detailed licensing information, please refer to the specific licenses accompanying the datasets provided in the table above.
All extra content purely authored by us is released under the MIT license:
Copyright (c) 2024 Idiap Research Institute.
MIT License.