File size: 23,007 Bytes
3f3c672 8642b7a c8b1548 8642b7a 3f3c672 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 |
---
annotations_creators:
- none
language_creators:
- unknown
language:
- en
license:
- apache-2.0
multilinguality:
- unknown
pretty_name: dstc10_track2_task2
size_categories:
- unknown
source_datasets:
- original
task_categories:
- dialog-response-generation
task_ids:
- unknown
---
# Dataset Card for GEM/dstc10_track2_task2
## Dataset Description
- **Homepage:** https://github.com/alexa/alexa-with-dstc10-track2-dataset
- **Repository:** https://github.com/alexa/alexa-with-dstc10-track2-dataset
- **Paper:** https://assets.amazon.science/54/a1/5282d47044179737b4289622c824/how-robust-are-you-evaluating-task-oriented-dialogue-systems-on-spoken-conversations.pdf
- **Leaderboard:** https://eval.ai/challenge/1663/overview
- **Point of Contact:** Seokhwan Kim
### Link to Main Data Card
You can find the main data card on the [GEM Website](https://gem-benchmark.com/data_cards/dstc10_track2_task2).
### 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](https://huggingface.co/datasets/GEM/dstc10_track2_task2).
#### website
https://github.com/alexa/alexa-with-dstc10-track2-dataset
#### paper
https://assets.amazon.science/54/a1/5282d47044179737b4289622c824/how-robust-are-you-evaluating-task-oriented-dialogue-systems-on-spoken-conversations.pdf
#### 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
<!-- info: What is the webpage for the dataset (if it exists)? -->
<!-- scope: telescope -->
https://github.com/alexa/alexa-with-dstc10-track2-dataset
#### Download
<!-- info: What is the link to where the original dataset is hosted? -->
<!-- scope: telescope -->
https://github.com/alexa/alexa-with-dstc10-track2-dataset
#### Paper
<!-- info: What is the link to the paper describing the dataset (open access preferred)? -->
<!-- scope: telescope -->
https://assets.amazon.science/54/a1/5282d47044179737b4289622c824/how-robust-are-you-evaluating-task-oriented-dialogue-systems-on-spoken-conversations.pdf
#### BibTex
<!-- info: Provide the BibTex-formatted reference for the dataset. Please use the correct published version (ACL anthology, etc.) instead of google scholar created Bibtex. -->
<!-- scope: microscope -->
@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
<!-- quick -->
<!-- info: If known, provide the name of at least one person the reader can contact for questions about the dataset. -->
<!-- scope: periscope -->
Seokhwan Kim
#### Contact Email
<!-- info: If known, provide the email of at least one person the reader can contact for questions about the dataset. -->
<!-- scope: periscope -->
seokhwk@amazon.com
#### Has a Leaderboard?
<!-- info: Does the dataset have an active leaderboard? -->
<!-- scope: telescope -->
yes
#### Leaderboard Link
<!-- info: Provide a link to the leaderboard. -->
<!-- scope: periscope -->
https://eval.ai/challenge/1663/overview
#### Leaderboard Details
<!-- info: Briefly describe how the leaderboard evaluates models. -->
<!-- scope: microscope -->
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?
<!-- quick -->
<!-- info: Is the dataset multilingual? -->
<!-- scope: telescope -->
no
#### Covered Languages
<!-- quick -->
<!-- info: What languages/dialects are covered in the dataset? -->
<!-- scope: telescope -->
`En`
#### License
<!-- quick -->
<!-- info: What is the license of the dataset? -->
<!-- scope: telescope -->
apache-2.0: Apache License 2.0
#### Intended Use
<!-- info: What is the intended use of the dataset? -->
<!-- scope: microscope -->
To conduct research on dialogue state tracking and knowledge-grounded response generation.
#### Primary Task
<!-- info: What primary task does the dataset support? -->
<!-- scope: telescope -->
Dialog Response Generation
#### Communicative Goal
<!-- quick -->
<!-- info: Provide a short description of the communicative goal of a model trained for this task on this dataset. -->
<!-- scope: periscope -->
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)
<!-- info: In what kind of organization did the dataset curation happen? -->
<!-- scope: telescope -->
`industry`
#### Curation Organization(s)
<!-- info: Name the organization(s). -->
<!-- scope: periscope -->
Amazon
#### Dataset Creators
<!-- info: Who created the original dataset? List the people involved in collecting the dataset and their affiliation(s). -->
<!-- scope: microscope -->
Seokhwan Kim, Yang Liu, Di Jin, Alexandros Papangelis, Karthik Gopalakrishnan, Behnam Hedayatnia, Dilek Hakkani-Tur (Amazon Alexa AI)
#### Funding
<!-- info: Who funded the data creation? -->
<!-- scope: microscope -->
Amazon
#### Who added the Dataset to GEM?
<!-- info: Who contributed to the data card and adding the dataset to GEM? List the people+affiliations involved in creating this data card and who helped integrate this dataset into GEM. -->
<!-- scope: microscope -->
Alexandros Papangelis (Amazon Alexa AI), Di Jin (Amazon Alexa AI), Nico Daheim (RWTH Aachen University)
### Dataset Structure
#### Data Fields
<!-- info: List and describe the fields present in the dataset. -->
<!-- scope: telescope -->
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
<!-- info: How was the dataset structure determined? -->
<!-- scope: microscope -->
It was kept compatible with MultiWox 2.X data.
#### Example Instance
<!-- info: Provide a JSON formatted example of a typical instance in the dataset. -->
<!-- scope: periscope -->
{'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': "<U> hi uh i'm looking for restaurant in lower ha <S> yeah definitely i can go ahead and help you with that ummm what kind of option in a restaurant are you looking for <U> yeah umm am looking for an expensive restaurant <S> 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 <U> sure ummm maybe if you don't have anything expensive how about something in the moderate price range <S> 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 <U> 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
<!-- info: Describe and name the splits in the dataset if there are more than one. -->
<!-- scope: periscope -->
train: training set, val: validation set, test: test set
#### Splitting Criteria
<!-- info: Describe any criteria for splitting the data, if used. If there are differences between the splits (e.g., if the training annotations are machine-generated and the dev and test ones are created by humans, or if different numbers of annotators contributed to each example), describe them here. -->
<!-- scope: microscope -->
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?
<!-- info: What does this dataset contribute toward better generation evaluation and why is it part of GEM? -->
<!-- scope: microscope -->
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
<!-- info: Do other datasets for the high level task exist? -->
<!-- scope: telescope -->
yes
#### Unique Language Coverage
<!-- info: Does this dataset cover other languages than other datasets for the same task? -->
<!-- scope: periscope -->
no
#### Difference from other GEM datasets
<!-- info: What else sets this dataset apart from other similar datasets in GEM? -->
<!-- scope: microscope -->
This dataset contains transcribed spoken interactions.
#### Ability that the Dataset measures
<!-- info: What aspect of model ability can be measured with this dataset? -->
<!-- scope: periscope -->
We can measure the model’s ability to understand language and to generate knowledge-grounded responses.
### GEM-Specific Curation
#### Modificatied for GEM?
<!-- info: Has the GEM version of the dataset been modified in any way (data, processing, splits) from the original curated data? -->
<!-- scope: telescope -->
no
#### Additional Splits?
<!-- info: Does GEM provide additional splits to the dataset? -->
<!-- scope: telescope -->
no
### Getting Started with the Task
## Previous Results
### Previous Results
#### Measured Model Abilities
<!-- info: What aspect of model ability can be measured with this dataset? -->
<!-- scope: telescope -->
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
<!-- info: What metrics are typically used for this task? -->
<!-- scope: periscope -->
`Other: Other Metrics`
#### Other Metrics
<!-- info: Definitions of other metrics -->
<!-- scope: periscope -->
BLEU-1, BLEU-2, BLEU-3, BLEU-4, METEOR, ROGUE-1, ROGUE-2, ROGUE-L
#### Previous results available?
<!-- info: Are previous results available? -->
<!-- scope: telescope -->
no
## Dataset Curation
### Original Curation
#### Original Curation Rationale
<!-- info: Original curation rationale -->
<!-- scope: telescope -->
We want to explore how conversational models perform on spoken data.
#### Communicative Goal
<!-- info: What was the communicative goal? -->
<!-- scope: periscope -->
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
<!-- info: Is the dataset aggregated from different data sources? -->
<!-- scope: telescope -->
no
### Language Data
#### How was Language Data Obtained?
<!-- info: How was the language data obtained? -->
<!-- scope: telescope -->
`Other`
#### Topics Covered
<!-- info: Does the language in the dataset focus on specific topics? How would you describe them? -->
<!-- scope: periscope -->
The conversations revolve around 5 domains (or topics): hotels, restaurants, attractions, taxi, train.
#### Data Validation
<!-- info: Was the text validated by a different worker or a data curator? -->
<!-- scope: telescope -->
not validated
#### Was Data Filtered?
<!-- info: Were text instances selected or filtered? -->
<!-- scope: telescope -->
not filtered
### Structured Annotations
#### Additional Annotations?
<!-- quick -->
<!-- info: Does the dataset have additional annotations for each instance? -->
<!-- scope: telescope -->
none
#### Annotation Service?
<!-- info: Was an annotation service used? -->
<!-- scope: telescope -->
no
### Consent
#### Any Consent Policy?
<!-- info: Was there a consent policy involved when gathering the data? -->
<!-- scope: telescope -->
yes
### Private Identifying Information (PII)
#### Contains PII?
<!-- quick -->
<!-- info: Does the source language data likely contain Personal Identifying Information about the data creators or subjects? -->
<!-- scope: telescope -->
no PII
#### Justification for no PII
<!-- info: Provide a justification for selecting `no PII` above. -->
<!-- scope: periscope -->
The subjects were instructed to conduct fictional conversations about booking restaurants or requesting fictional information.
### Maintenance
#### Any Maintenance Plan?
<!-- info: Does the original dataset have a maintenance plan? -->
<!-- scope: telescope -->
no
## Broader Social Context
### Previous Work on the Social Impact of the Dataset
#### Usage of Models based on the Data
<!-- info: Are you aware of cases where models trained on the task featured in this dataset ore related tasks have been used in automated systems? -->
<!-- scope: telescope -->
no
### Impact on Under-Served Communities
#### Addresses needs of underserved Communities?
<!-- info: Does this dataset address the needs of communities that are traditionally underserved in language technology, and particularly language generation technology? Communities may be underserved for exemple because their language, language variety, or social or geographical context is underepresented in NLP and NLG resources (datasets and models). -->
<!-- scope: telescope -->
no
### Discussion of Biases
#### Any Documented Social Biases?
<!-- info: Are there documented social biases in the dataset? Biases in this context are variations in the ways members of different social categories are represented that can have harmful downstream consequences for members of the more disadvantaged group. -->
<!-- scope: telescope -->
unsure
## Considerations for Using the Data
### PII Risks and Liability
#### Potential PII Risk
<!-- info: Considering your answers to the PII part of the Data Curation Section, describe any potential privacy to the data subjects and creators risks when using the dataset. -->
<!-- scope: microscope -->
There should be no risk related to PII as the subjects conduct fictional conversations.
### Licenses
#### Copyright Restrictions on the Dataset
<!-- info: Based on your answers in the Intended Use part of the Data Overview Section, which of the following best describe the copyright and licensing status of the dataset? -->
<!-- scope: periscope -->
`open license - commercial use allowed`
#### Copyright Restrictions on the Language Data
<!-- info: Based on your answers in the Language part of the Data Curation Section, which of the following best describe the copyright and licensing status of the underlying language data? -->
<!-- scope: periscope -->
`open license - commercial use allowed`
### Known Technical Limitations
|