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Dataset Card for GEM/dstc10_track2_task2
Link to Main Data Card
You can find the main data card on the GEM Website.
Dataset Summary
The DSTC10 Track2 Task 2 follows the DSTC9 Track1 task, where participants have to implement knowledge-grounded dialog systems. The training dataset is inherited from the DSTC9 challenge and is in the written domain, while the test set is newly collected and consists of noisy ASR transcripts. Hence, the dataset facilitates building models for grounded dialog response generation.
You can load the dataset via:
import datasets
data = datasets.load_dataset('GEM/dstc10_track2_task2')
The data loader can be found here.
website
https://github.com/alexa/alexa-with-dstc10-track2-dataset
paper
authors
Seokhwan Kim, Yang Liu, Di Jin, Alexandros Papangelis, Karthik Gopalakrishnan, Behnam Hedayatnia, Dilek Hakkani-Tur (Amazon Alexa AI)
Dataset Overview
Where to find the Data and its Documentation
Webpage
https://github.com/alexa/alexa-with-dstc10-track2-dataset
Download
https://github.com/alexa/alexa-with-dstc10-track2-dataset
Paper
BibTex
@inproceedings{kim2021robust, title={" How Robust ru?": Evaluating Task-Oriented Dialogue Systems on Spoken Conversations}, author={Kim, Seokhwan and Liu, Yang and Jin, Di and Papangelis, Alexandros and Gopalakrishnan, Karthik and Hedayatnia, Behnam and Hakkani-Tur, Dilek}, journal={IEEE Automatic Speech Recognition and Understanding Workshop}, year={2021} }
Contact Name
Seokhwan Kim
Contact Email
Has a Leaderboard?
yes
Leaderboard Link
https://eval.ai/challenge/1663/overview
Leaderboard Details
It evaluates the models based on the automatic metrics defined in the task paper for the three tasks of detection, selection and generation.
Languages and Intended Use
Multilingual?
no
Covered Languages
En
License
apache-2.0: Apache License 2.0
Intended Use
To conduct research on dialogue state tracking and knowledge-grounded response generation.
Primary Task
Dialog Response Generation
Communicative Goal
This dataset aims to explore the robustness of conversational models when trained on spoken data. It has two aspects, multi-domain dialogue state tracking and conversation modeling with access to unstructured knowledge.
Credit
Curation Organization Type(s)
industry
Curation Organization(s)
Amazon
Dataset Creators
Seokhwan Kim, Yang Liu, Di Jin, Alexandros Papangelis, Karthik Gopalakrishnan, Behnam Hedayatnia, Dilek Hakkani-Tur (Amazon Alexa AI)
Funding
Amazon
Who added the Dataset to GEM?
Alexandros Papangelis (Amazon Alexa AI), Di Jin (Amazon Alexa AI), Nico Daheim (RWTH Aachen University)
Dataset Structure
Data Fields
features = datasets.Features(
{
"id": datasets.Value("string"),
"gem_id": datasets.Value("string"),
"turns": [
{
"speaker": datasets.Value("string"),
"text": datasets.Value("string"),
"nbest": [
{
"hyp": datasets.Value("string"),
"score": datasets.Value("float"),
}
],
}
],
"knowledge": {
"domain": datasets.Value("string"),
"entity_name": datasets.Value("string"),
"title": datasets.Value("string"),
"body": datasets.Value("string"),
},
"response": datasets.Value("string"),
"source": datasets.Value("string"),
"linearized_input": datasets.Value("string"),
"target": datasets.Value("string"),
"references": [datasets.Value("string")],
}
)
nbest contains an nbest list of outputs generated by an ASR system along with their scores.
knowledge defines the annotated grounding as well as its metadata
Reason for Structure
It was kept compatible with MultiWox 2.X data.
Example Instance
{'id': '0',
'gem_id': 'GEM-dstc10_track2_task2-test-0',
'turns': [{'speaker': 'U',
'text': "hi uh i'm looking for restaurant in lower ha",
'nbest': [{'hyp': "hi uh i'm looking for restaurant in lower ha",
'score': -25.625450134277344},
{'hyp': "hi uh i'm looking for restaurant in lower hai",
'score': -25.969446182250977},
{'hyp': "hi uh i'm looking for restaurant in lower haig",
'score': -32.816890716552734},
{'hyp': "hi uh i'm looking for restaurant in lower haigh",
'score': -32.84316635131836},
{'hyp': "hi uh i'm looking for restaurant in lower hag",
'score': -32.8637580871582},
{'hyp': "hi uh i'm looking for restaurant in lower hah",
'score': -33.1048698425293},
{'hyp': "hi uh i'm looking for restaurant in lower hait",
'score': -33.96509552001953},
{'hyp': "hi um i'm looking for restaurant in lower hai",
'score': -33.97885513305664},
{'hyp': "hi um i'm looking for restaurant in lower haig",
'score': -34.56083679199219},
{'hyp': "hi um i'm looking for restaurant in lower haigh",
'score': -34.58711242675781}]},
{'speaker': 'S',
'text': 'yeah definitely i can go ahead and help you with that ummm what kind of option in a restaurant are you looking for',
'nbest': []},
{'speaker': 'U',
'text': 'yeah umm am looking for an expensive restaurant',
'nbest': [{'hyp': 'yeah umm am looking for an expensive restaurant',
'score': -21.272899627685547},
{'hyp': 'yeah umm m looking for an expensive restaurant',
'score': -21.444047927856445},
{'hyp': 'yeah umm a m looking for an expensive restaurant',
'score': -21.565458297729492},
{'hyp': 'yeah ummm am looking for an expensive restaurant',
'score': -21.68832778930664},
{'hyp': 'yeah ummm m looking for an expensive restaurant',
'score': -21.85947608947754},
{'hyp': 'yeah ummm a m looking for an expensive restaurant',
'score': -21.980886459350586},
{'hyp': "yeah umm a'm looking for an expensive restaurant",
'score': -22.613924026489258},
{'hyp': "yeah ummm a'm looking for an expensive restaurant",
'score': -23.02935218811035},
{'hyp': 'yeah um am looking for an expensive restaurant',
'score': -23.11180305480957},
{'hyp': 'yeah um m looking for an expensive restaurant',
'score': -23.28295135498047}]},
{'speaker': 'S',
'text': "lemme go ahead and see what i can find for you ok great so i do ummm actually no i'm sorry is there something else i can help you find i don't see anything expensive",
'nbest': []},
{'speaker': 'U',
'text': "sure ummm maybe if you don't have anything expensive how about something in the moderate price range",
'nbest': [{'hyp': "sure ummm maybe if you don't have anything expensive how about something in the moderate price range",
'score': -27.492507934570312},
{'hyp': "sure umm maybe if you don't have anything expensive how about something in the moderate price range",
'score': -27.75853729248047},
{'hyp': "sure ummm maybe if you don't have anything expensive how about something in the moderate price rang",
'score': -29.44410514831543},
{'hyp': "sure umm maybe if you don't have anything expensive how about something in the moderate price rang",
'score': -29.710134506225586},
{'hyp': "sure um maybe if you don't have anything expensive how about something in the moderate price range",
'score': -31.136560440063477},
{'hyp': "sure um maybe if you don't have anything expensive how about something in the moderate price rang",
'score': -33.088157653808594},
{'hyp': "sure ummm maybe i you don't have anything expensive how about something in the moderate price range",
'score': -36.127620697021484},
{'hyp': "sure umm maybe i you don't have anything expensive how about something in the moderate price range",
'score': -36.39365005493164},
{'hyp': "sure ummm maybe if yo don't have anything expensive how about something in the moderate price range",
'score': -36.43605041503906},
{'hyp': "sure umm maybe if yo don't have anything expensive how about something in the moderate price range",
'score': -36.70207977294922}]},
{'speaker': 'S',
'text': 'ok moderate lemme go ahead and check to see what i can find for moderate ok great i do have several options coming up how does the view lounge sound',
'nbest': []},
{'speaker': 'U',
'text': 'that sounds good ummm do they have any sort of happy hour special',
'nbest': [{'hyp': 'that sounds good ummm do they have any sort of happy hour special',
'score': -30.316478729248047},
{'hyp': 'that sounds good umm do they have any sort of happy hour special',
'score': -30.958009719848633},
{'hyp': 'that sounds good um do they have any sort of happy hour special',
'score': -34.463165283203125},
{'hyp': 'that sounds good ummm do they have any sirt of happy hour special',
'score': -34.48350143432617},
{'hyp': 'that sounds good umm do they have any sirt of happy hour special',
'score': -35.12503433227539},
{'hyp': 'that sounds good ummm do they have any sord of happy hour special',
'score': -35.61939239501953},
{'hyp': 'that sounds good umm do they have any sord of happy hour special',
'score': -36.26092529296875},
{'hyp': 'that sounds good ummm do they have any sont of happy hour special',
'score': -37.697105407714844},
{'hyp': 'that sounds good umm do they have any sont of happy hour special',
'score': -38.33863830566406},
{'hyp': 'that sounds good um do they have any sirt of happy hour special',
'score': -38.630191802978516}]}],
'knowledge': {'domain': 'restaurant',
'entity_name': 'The View Lounge',
'title': 'Does The View Lounge offer happy hour?',
'body': 'The View Lounge offers happy hour.'},
'response': 'uhhh great question lemme go ahead and check that out for you ok fantastic so it looks like they do offer happy hour',
'source': 'sf_spoken',
'linearized_input': " hi uh i'm looking for restaurant in lower ha yeah definitely i can go ahead and help you with that ummm what kind of option in a restaurant are you looking for yeah umm am looking for an expensive restaurant lemme go ahead and see what i can find for you ok great so i do ummm actually no i'm sorry is there something else i can help you find i don't see anything expensive sure ummm maybe if you don't have anything expensive how about something in the moderate price range ok moderate lemme go ahead and check to see what i can find for moderate ok great i do have several options coming up how does the view lounge sound that sounds good ummm do they have any sort of happy hour special || knowledge domain: restaurant, entity: The View Lounge, title: Does The View Lounge offer happy hour?, information: The View Lounge offers happy hour.",
'target': 'uhhh great question lemme go ahead and check that out for you ok fantastic so it looks like they do offer happy hour',
'references': ['uhhh great question lemme go ahead and check that out for you ok fantastic so it looks like they do offer happy hour']}
Data Splits
train: training set, val: validation set, test: test set
Splitting Criteria
The track dataset originally only consists of a validation and test set in the spoken domain with noisy ASR transcripts. The training set is taken from the predecessor task DSTC9 Track 1 and contains written conversations.
Dataset in GEM
Rationale for Inclusion in GEM
Why is the Dataset in GEM?
This dataset can be used to evaluate conversational models on spoken inputs (using ASR hypotheses). In particular, we can evaluate the models’ ability to understand language by tracking the dialogue state, and their ability to generate knowledge-grounded responses.
Similar Datasets
yes
Unique Language Coverage
no
Difference from other GEM datasets
This dataset contains transcribed spoken interactions.
Ability that the Dataset measures
We can measure the model’s ability to understand language and to generate knowledge-grounded responses.
GEM-Specific Curation
Modificatied for GEM?
no
Additional Splits?
no
Getting Started with the Task
Previous Results
Previous Results
Measured Model Abilities
This dataset can be used to evaluate conversational models on spoken inputs (using ASR hypotheses). In particular, we can evaluate the models’ ability to generate knowledge-grounded responses.
Metrics
Other: Other Metrics
Other Metrics
BLEU-1, BLEU-2, BLEU-3, BLEU-4, METEOR, ROGUE-1, ROGUE-2, ROGUE-L
Previous results available?
no
Dataset Curation
Original Curation
Original Curation Rationale
We want to explore how conversational models perform on spoken data.
Communicative Goal
This dataset aims to explore the robustness of conversational models when evaluated on spoken data. It has two aspects, multi-domain dialogue state tracking and conversation modeling with access to unstructured knowledge.
Sourced from Different Sources
no
Language Data
How was Language Data Obtained?
Other
Topics Covered
The conversations revolve around 5 domains (or topics): hotels, restaurants, attractions, taxi, train.
Data Validation
not validated
Was Data Filtered?
not filtered
Structured Annotations
Additional Annotations?
none
Annotation Service?
no
Consent
Any Consent Policy?
yes
Private Identifying Information (PII)
Contains PII?
no PII
Justification for no PII
The subjects were instructed to conduct fictional conversations about booking restaurants or requesting fictional information.
Maintenance
Any Maintenance Plan?
no
Broader Social Context
Previous Work on the Social Impact of the Dataset
Usage of Models based on the Data
no
Impact on Under-Served Communities
Addresses needs of underserved Communities?
no
Discussion of Biases
Any Documented Social Biases?
unsure
Considerations for Using the Data
PII Risks and Liability
Potential PII Risk
There should be no risk related to PII as the subjects conduct fictional conversations.
Licenses
Copyright Restrictions on the Dataset
open license - commercial use allowed
Copyright Restrictions on the Language Data
open license - commercial use allowed
Known Technical Limitations
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