Datasets:
Tasks:
Text Classification
Sub-tasks:
multi-label-classification
Languages:
English
Size:
100K<n<1M
ArXiv:
License:
File size: 25,472 Bytes
dd486cd 19a4dbf dd486cd 19a4dbf 9b0e986 dd486cd fbc850a dd486cd 46562bf 6c2a2c3 cdb700c 6c2a2c3 cdb700c 6c2a2c3 2f6ff01 6c2a2c3 dd486cd 46562bf dd486cd 671842f dd486cd 56b9abd dd486cd 493cb2a dd486cd 493cb2a dd486cd 46562bf dd486cd 46562bf dd486cd fbc850a dd486cd 46562bf fbc850a dd486cd 56b9abd dd486cd 56b9abd 46562bf 56b9abd dd486cd 56b9abd dd486cd 46562bf dd486cd 56b9abd dd486cd 56b9abd dd486cd 56b9abd dd486cd 56b9abd 6c2a2c3 |
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 |
---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- cc-by-nc-sa-3.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- extended|other-Switchboard-1 Telephone Speech Corpus, Release 2
task_categories:
- text-classification
task_ids:
- multi-label-classification
pretty_name: The Switchboard Dialog Act Corpus (SwDA)
dataset_info:
features:
- name: swda_filename
dtype: string
- name: ptb_basename
dtype: string
- name: conversation_no
dtype: int64
- name: transcript_index
dtype: int64
- name: act_tag
dtype:
class_label:
names:
'0': b^m^r
'1': qw^r^t
'2': aa^h
'3': br^m
'4': fa^r
'5': aa,ar
'6': sd^e(^q)^r
'7': ^2
'8': sd;qy^d
'9': oo
'10': bk^m
'11': aa^t
'12': cc^t
'13': qy^d^c
'14': qo^t
'15': ng^m
'16': qw^h
'17': qo^r
'18': aa
'19': qy^d^t
'20': qrr^d
'21': br^r
'22': fx
'23': sd,qy^g
'24': ny^e
'25': ^h^t
'26': fc^m
'27': qw(^q)
'28': co
'29': o^t
'30': b^m^t
'31': qr^d
'32': qw^g
'33': ad(^q)
'34': qy(^q)
'35': na^r
'36': am^r
'37': qr^t
'38': ad^c
'39': qw^c
'40': bh^r
'41': h^t
'42': ft^m
'43': ba^r
'44': qw^d^t
'45': '%'
'46': t3
'47': nn
'48': bd
'49': h^m
'50': h^r
'51': sd^r
'52': qh^m
'53': ^q^t
'54': sv^2
'55': ft
'56': ar^m
'57': qy^h
'58': sd^e^m
'59': qh^r
'60': cc
'61': fp^m
'62': ad
'63': qo
'64': na^m^t
'65': fo^c
'66': qy
'67': sv^e^r
'68': aap
'69': 'no'
'70': aa^2
'71': sv(^q)
'72': sv^e
'73': nd
'74': '"'
'75': bf^2
'76': bk
'77': fp
'78': nn^r^t
'79': fa^c
'80': ny^t
'81': ny^c^r
'82': qw
'83': qy^t
'84': b
'85': fo
'86': qw^r
'87': am
'88': bf^t
'89': ^2^t
'90': b^2
'91': x
'92': fc
'93': qr
'94': no^t
'95': bk^t
'96': bd^r
'97': bf
'98': ^2^g
'99': qh^c
'100': ny^c
'101': sd^e^r
'102': br
'103': fe
'104': by
'105': ^2^r
'106': fc^r
'107': b^m
'108': sd,sv
'109': fa^t
'110': sv^m
'111': qrr
'112': ^h^r
'113': na
'114': fp^r
'115': o
'116': h,sd
'117': t1^t
'118': nn^r
'119': cc^r
'120': sv^c
'121': co^t
'122': qy^r
'123': sv^r
'124': qy^d^h
'125': sd
'126': nn^e
'127': ny^r
'128': b^t
'129': ba^m
'130': ar
'131': bf^r
'132': sv
'133': bh^m
'134': qy^g^t
'135': qo^d^c
'136': qo^d
'137': nd^t
'138': aa^r
'139': sd^2
'140': sv;sd
'141': qy^c^r
'142': qw^m
'143': qy^g^r
'144': no^r
'145': qh(^q)
'146': sd;sv
'147': bf(^q)
'148': +
'149': qy^2
'150': qw^d
'151': qy^g
'152': qh^g
'153': nn^t
'154': ad^r
'155': oo^t
'156': co^c
'157': ng
'158': ^q
'159': qw^d^c
'160': qrr^t
'161': ^h
'162': aap^r
'163': bc^r
'164': sd^m
'165': bk^r
'166': qy^g^c
'167': qr(^q)
'168': ng^t
'169': arp
'170': h
'171': bh
'172': sd^c
'173': ^g
'174': o^r
'175': qy^c
'176': sd^e
'177': fw
'178': ar^r
'179': qy^m
'180': bc
'181': sv^t
'182': aap^m
'183': sd;no
'184': ng^r
'185': bf^g
'186': sd^e^t
'187': o^c
'188': b^r
'189': b^m^g
'190': ba
'191': t1
'192': qy^d(^q)
'193': nn^m
'194': ny
'195': ba,fe
'196': aa^m
'197': qh
'198': na^m
'199': oo(^q)
'200': qw^t
'201': na^t
'202': qh^h
'203': qy^d^m
'204': ny^m
'205': fa
'206': qy^d
'207': fc^t
'208': sd(^q)
'209': qy^d^r
'210': bf^m
'211': sd(^q)^t
'212': ft^t
'213': ^q^r
'214': sd^t
'215': sd(^q)^r
'216': ad^t
- name: damsl_act_tag
dtype:
class_label:
names:
'0': ad
'1': qo
'2': qy
'3': arp_nd
'4': sd
'5': h
'6': bh
'7': 'no'
'8': ^2
'9': ^g
'10': ar
'11': aa
'12': sv
'13': bk
'14': fp
'15': qw
'16': b
'17': ba
'18': t1
'19': oo_co_cc
'20': +
'21': ny
'22': qw^d
'23': x
'24': qh
'25': fc
'26': fo_o_fw_"_by_bc
'27': aap_am
'28': '%'
'29': bf
'30': t3
'31': nn
'32': bd
'33': ng
'34': ^q
'35': br
'36': qy^d
'37': fa
'38': ^h
'39': b^m
'40': ft
'41': qrr
'42': na
- name: caller
dtype: string
- name: utterance_index
dtype: int64
- name: subutterance_index
dtype: int64
- name: text
dtype: string
- name: pos
dtype: string
- name: trees
dtype: string
- name: ptb_treenumbers
dtype: string
- name: talk_day
dtype: string
- name: length
dtype: int64
- name: topic_description
dtype: string
- name: prompt
dtype: string
- name: from_caller
dtype: int64
- name: from_caller_sex
dtype: string
- name: from_caller_education
dtype: int64
- name: from_caller_birth_year
dtype: int64
- name: from_caller_dialect_area
dtype: string
- name: to_caller
dtype: int64
- name: to_caller_sex
dtype: string
- name: to_caller_education
dtype: int64
- name: to_caller_birth_year
dtype: int64
- name: to_caller_dialect_area
dtype: string
splits:
- name: train
num_bytes: 128498512
num_examples: 213543
- name: validation
num_bytes: 34749819
num_examples: 56729
- name: test
num_bytes: 2560127
num_examples: 4514
download_size: 14456364
dataset_size: 165808458
---
# Dataset Card for SwDA
## 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
- **Homepage:** [The Switchboard Dialog Act Corpus](http://compprag.christopherpotts.net/swda.html)
- **Repository:** [NathanDuran/Switchboard-Corpus](https://github.com/cgpotts/swda)
- **Paper:** [The Switchboard Dialog Act Corpus](http://compprag.christopherpotts.net/swda.html)
= **Leaderboard: [Dialogue act classification](https://github.com/sebastianruder/NLP-progress/blob/master/english/dialogue.md#dialogue-act-classification)**
- **Point of Contact:** [Christopher Potts](https://web.stanford.edu/~cgpotts/)
### Dataset Summary
The Switchboard Dialog Act Corpus (SwDA) extends the Switchboard-1 Telephone Speech Corpus, Release 2 with
turn/utterance-level dialog-act tags. The tags summarize syntactic, semantic, and pragmatic information about the
associated turn. The SwDA project was undertaken at UC Boulder in the late 1990s.
The SwDA is not inherently linked to the Penn Treebank 3 parses of Switchboard, and it is far from straightforward to
align the two resources. In addition, the SwDA is not distributed with the Switchboard's tables of metadata about the
conversations and their participants.
### Supported Tasks and Leaderboards
| Model | Accuracy | Paper / Source | Code |
| ------------- | :-----:| --- | --- |
| H-Seq2seq (Colombo et al., 2020) | 85.0 | [Guiding attention in Sequence-to-sequence models for Dialogue Act prediction](https://ojs.aaai.org/index.php/AAAI/article/view/6259/6115)
| SGNN (Ravi et al., 2018) | 83.1 | [Self-Governing Neural Networks for On-Device Short Text Classification](https://www.aclweb.org/anthology/D18-1105.pdf)
| CASA (Raheja et al., 2019) | 82.9 | [Dialogue Act Classification with Context-Aware Self-Attention](https://www.aclweb.org/anthology/N19-1373.pdf)
| DAH-CRF (Li et al., 2019) | 82.3 | [A Dual-Attention Hierarchical Recurrent Neural Network for Dialogue Act Classification](https://www.aclweb.org/anthology/K19-1036.pdf)
| ALDMN (Wan et al., 2018) | 81.5 | [Improved Dynamic Memory Network for Dialogue Act Classification with Adversarial Training](https://arxiv.org/pdf/1811.05021.pdf)
| CRF-ASN (Chen et al., 2018) | 81.3 | [Dialogue Act Recognition via CRF-Attentive Structured Network](https://arxiv.org/abs/1711.05568)
| Pretrained H-Transformer (Chapuis et al., 2020) | 79.3 | [Hierarchical Pre-training for Sequence Labelling in Spoken Dialog] (https://www.aclweb.org/anthology/2020.findings-emnlp.239)
| Bi-LSTM-CRF (Kumar et al., 2017) | 79.2 | [Dialogue Act Sequence Labeling using Hierarchical encoder with CRF](https://arxiv.org/abs/1709.04250) | [Link](https://github.com/YanWenqiang/HBLSTM-CRF) |
| RNN with 3 utterances in context (Bothe et al., 2018) | 77.34 | [A Context-based Approach for Dialogue Act Recognition using Simple Recurrent Neural Networks](https://arxiv.org/abs/1805.06280) | |
### Languages
The language supported is English.
## Dataset Structure
Utterance are tagged with the [SWBD-DAMSL](https://web.stanford.edu/~jurafsky/ws97/manual.august1.html) DA.
### Data Instances
An example from the dataset is:
`{'act_tag': 115, 'caller': 'A', 'conversation_no': 4325, 'damsl_act_tag': 26, 'from_caller': 1632, 'from_caller_birth_year': 1962, 'from_caller_dialect_area': 'WESTERN', 'from_caller_education': 2, 'from_caller_sex': 'FEMALE', 'length': 5, 'pos': 'Okay/UH ./.', 'prompt': 'FIND OUT WHAT CRITERIA THE OTHER CALLER WOULD USE IN SELECTING CHILD CARE SERVICES FOR A PRESCHOOLER. IS IT EASY OR DIFFICULT TO FIND SUCH CARE?', 'ptb_basename': '4/sw4325', 'ptb_treenumbers': '1', 'subutterance_index': 1, 'swda_filename': 'sw00utt/sw_0001_4325.utt', 'talk_day': '03/23/1992', 'text': 'Okay. /', 'to_caller': 1519, 'to_caller_birth_year': 1971, 'to_caller_dialect_area': 'SOUTH MIDLAND', 'to_caller_education': 1, 'to_caller_sex': 'FEMALE', 'topic_description': 'CHILD CARE', 'transcript_index': 0, 'trees': '(INTJ (UH Okay) (. .) (-DFL- E_S))', 'utterance_index': 1}`
### Data Fields
* `swda_filename`: (str) The filename: directory/basename.
* `ptb_basename`: (str) The Treebank filename: add ".pos" for POS and ".mrg" for trees
* `conversation_no`: (int) The conversation Id, to key into the metadata database.
* `transcript_index`: (int) The line number of this item in the transcript (counting only utt lines).
* `act_tag`: (list of str) The Dialog Act Tags (separated by ||| in the file). Check Dialog act annotations for more details.
* `damsl_act_tag`: (list of str) The Dialog Act Tags of the 217 variation tags.
* `caller`: (str) A, B, @A, @B, @@A, @@B
* `utterance_index`: (int) The encoded index of the utterance (the number in A.49, B.27, etc.)
* `subutterance_index`: (int) Utterances can be broken across line. This gives the internal position.
* `text`: (str) The text of the utterance
* `pos`: (str) The POS tagged version of the utterance, from PtbBasename+.pos
* `trees`: (str) The tree(s) containing this utterance (separated by ||| in the file). Use `[Tree.fromstring(t) for t in row_value.split("|||")]` to convert to (list of nltk.tree.Tree).
* `ptb_treenumbers`: (list of int) The tree numbers in the PtbBasename+.mrg
* `talk_day`: (str) Date of talk.
* `length`: (int) Length of talk in seconds.
* `topic_description`: (str) Short description of topic that's being discussed.
* `prompt`: (str) Long decription/query/instruction.
* `from_caller`: (int) The numerical Id of the from (A) caller.
* `from_caller_sex`: (str) MALE, FEMALE.
* `from_caller_education`: (int) Called education level 0, 1, 2, 3, 9.
* `from_caller_birth_year`: (int) Caller birth year YYYY.
* `from_caller_dialect_area`: (str) MIXED, NEW ENGLAND, NORTH MIDLAND, NORTHERN, NYC, SOUTH MIDLAND, SOUTHERN, UNK, WESTERN.
* `to_caller`: (int) The numerical Id of the to (B) caller.
* `to_caller_sex`: (str) MALE, FEMALE.
* `to_caller_education`: (int) Called education level 0, 1, 2, 3, 9.
* `to_caller_birth_year`: (int) Caller birth year YYYY.
* `to_caller_dialect_area`: (str) MIXED, NEW ENGLAND, NORTH MIDLAND, NORTHERN, NYC, SOUTH MIDLAND, SOUTHERN, UNK, WESTERN.
### Dialog act annotations
| | name | act_tag | example | train_count | full_count |
|----- |------------------------------- |---------------- |-------------------------------------------------- |------------- |------------ |
| 1 | Statement-non-opinion | sd | Me, I'm in the legal department. | 72824 | 75145 |
| 2 | Acknowledge (Backchannel) | b | Uh-huh. | 37096 | 38298 |
| 3 | Statement-opinion | sv | I think it's great | 25197 | 26428 |
| 4 | Agree/Accept | aa | That's exactly it. | 10820 | 11133 |
| 5 | Abandoned or Turn-Exit | % | So, - | 10569 | 15550 |
| 6 | Appreciation | ba | I can imagine. | 4633 | 4765 |
| 7 | Yes-No-Question | qy | Do you have to have any special training? | 4624 | 4727 |
| 8 | Non-verbal | x | [Laughter], [Throat_clearing] | 3548 | 3630 |
| 9 | Yes answers | ny | Yes. | 2934 | 3034 |
| 10 | Conventional-closing | fc | Well, it's been nice talking to you. | 2486 | 2582 |
| 11 | Uninterpretable | % | But, uh, yeah | 2158 | 15550 |
| 12 | Wh-Question | qw | Well, how old are you? | 1911 | 1979 |
| 13 | No answers | nn | No. | 1340 | 1377 |
| 14 | Response Acknowledgement | bk | Oh, okay. | 1277 | 1306 |
| 15 | Hedge | h | I don't know if I'm making any sense or not. | 1182 | 1226 |
| 16 | Declarative Yes-No-Question | qy^d | So you can afford to get a house? | 1174 | 1219 |
| 17 | Other | fo_o_fw_by_bc | Well give me a break, you know. | 1074 | 883 |
| 18 | Backchannel in question form | bh | Is that right? | 1019 | 1053 |
| 19 | Quotation | ^q | You can't be pregnant and have cats | 934 | 983 |
| 20 | Summarize/reformulate | bf | Oh, you mean you switched schools for the kids. | 919 | 952 |
| 21 | Affirmative non-yes answers | na | It is. | 836 | 847 |
| 22 | Action-directive | ad | Why don't you go first | 719 | 746 |
| 23 | Collaborative Completion | ^2 | Who aren't contributing. | 699 | 723 |
| 24 | Repeat-phrase | b^m | Oh, fajitas | 660 | 688 |
| 25 | Open-Question | qo | How about you? | 632 | 656 |
| 26 | Rhetorical-Questions | qh | Who would steal a newspaper? | 557 | 575 |
| 27 | Hold before answer/agreement | ^h | I'm drawing a blank. | 540 | 556 |
| 28 | Reject | ar | Well, no | 338 | 346 |
| 29 | Negative non-no answers | ng | Uh, not a whole lot. | 292 | 302 |
| 30 | Signal-non-understanding | br | Excuse me? | 288 | 298 |
| 31 | Other answers | no | I don't know | 279 | 286 |
| 32 | Conventional-opening | fp | How are you? | 220 | 225 |
| 33 | Or-Clause | qrr | or is it more of a company? | 207 | 209 |
| 34 | Dispreferred answers | arp_nd | Well, not so much that. | 205 | 207 |
| 35 | 3rd-party-talk | t3 | My goodness, Diane, get down from there. | 115 | 117 |
| 36 | Offers, Options, Commits | oo_co_cc | I'll have to check that out | 109 | 110 |
| 37 | Self-talk | t1 | What's the word I'm looking for | 102 | 103 |
| 38 | Downplayer | bd | That's all right. | 100 | 103 |
| 39 | Maybe/Accept-part | aap_am | Something like that | 98 | 105 |
| 40 | Tag-Question | ^g | Right? | 93 | 92 |
| 41 | Declarative Wh-Question | qw^d | You are what kind of buff? | 80 | 80 |
| 42 | Apology | fa | I'm sorry. | 76 | 79 |
| 43 | Thanking | ft | Hey thanks a lot | 67 | 78 |
### Data Splits
I used info from the [Probabilistic-RNN-DA-Classifier](https://github.com/NathanDuran/Probabilistic-RNN-DA-Classifier) repo:
The same training and test splits as used by [Stolcke et al. (2000)](https://web.stanford.edu/~jurafsky/ws97).
The development set is a subset of the training set to speed up development and testing used in the paper [Probabilistic Word Association for Dialogue Act Classification with Recurrent Neural Networks](https://www.researchgate.net/publication/326640934_Probabilistic_Word_Association_for_Dialogue_Act_Classification_with_Recurrent_Neural_Networks_19th_International_Conference_EANN_2018_Bristol_UK_September_3-5_2018_Proceedings).
|Dataset |# Transcripts |# Utterances |
|-----------|:-------------:|:-------------:|
|Training |1115 |192,768 |
|Validation |21 |3,196 |
|Test |19 |4,088 |
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
The SwDA is not inherently linked to the Penn Treebank 3 parses of Switchboard, and it is far from straightforward to align the two resources Calhoun et al. 2010, §2.4. In addition, the SwDA is not distributed with the Switchboard's tables of metadata about the conversations and their participants.
#### 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
[Christopher Potts](https://web.stanford.edu/~cgpotts/), Stanford Linguistics.
### Licensing Information
This work is licensed under a [Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License.](http://creativecommons.org/licenses/by-nc-sa/3.0/)
### Citation Information
```
@techreport{Jurafsky-etal:1997,
Address = {Boulder, CO},
Author = {Jurafsky, Daniel and Shriberg, Elizabeth and Biasca, Debra},
Institution = {University of Colorado, Boulder Institute of Cognitive Science},
Number = {97-02},
Title = {Switchboard {SWBD}-{DAMSL} Shallow-Discourse-Function Annotation Coders Manual, Draft 13},
Year = {1997}}
@article{Shriberg-etal:1998,
Author = {Shriberg, Elizabeth and Bates, Rebecca and Taylor, Paul and Stolcke, Andreas and Jurafsky, Daniel and Ries, Klaus and Coccaro, Noah and Martin, Rachel and Meteer, Marie and Van Ess-Dykema, Carol},
Journal = {Language and Speech},
Number = {3--4},
Pages = {439--487},
Title = {Can Prosody Aid the Automatic Classification of Dialog Acts in Conversational Speech?},
Volume = {41},
Year = {1998}}
@article{Stolcke-etal:2000,
Author = {Stolcke, Andreas and Ries, Klaus and Coccaro, Noah and Shriberg, Elizabeth and Bates, Rebecca and Jurafsky, Daniel and Taylor, Paul and Martin, Rachel and Meteer, Marie and Van Ess-Dykema, Carol},
Journal = {Computational Linguistics},
Number = {3},
Pages = {339--371},
Title = {Dialogue Act Modeling for Automatic Tagging and Recognition of Conversational Speech},
Volume = {26},
Year = {2000}}
```
### Contributions
Thanks to [@gmihaila](https://github.com/gmihaila) for adding this dataset. |