Hugging Face's logo

Datasets:
cgpotts
/
swda

Tasks:
Text Classification
Sub-tasks:
multi-label-classification
Languages:
English
Size:
100K<n<1M
ArXiv:
License:
Dataset card Files Community
3
swda / README.md
system's picture
system HF staff
Update files from the datasets library (from 1.2.1)
dd486cd
preview code
|
raw
history blame
No virus
17.2 kB
metadata
annotations_creators:
  - found
language_creators:
  - found
languages:
  - en
licenses:
  - cc-by-nc-sa-3-0
multilinguality:
  - monolingual
size_categories:
  - n<1K
source_datasets:
  - extended|other-Switchboard-1 Telephone Speech Corpus, Release 2
task_categories:
  - text-classification
task_ids:
  - multi-label-classification

Dataset Card for swda

Table of Contents

Dataset Description

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
SGNN (Ravi et al., 2018) 83.1 Self-Governing Neural Networks for On-Device Short Text Classification
CASA (Raheja et al., 2019) 82.9 Dialogue Act Classification with Context-Aware Self-Attention
DAH-CRF (Li et al., 2019) 82.3 A Dual-Attention Hierarchical Recurrent Neural Network for Dialogue Act Classification
ALDMN (Wan et al., 2018) 81.5 Improved Dynamic Memory Network for Dialogue Act Classification with Adversarial Training
CRF-ASN (Chen et al., 2018) 81.3 Dialogue Act Recognition via CRF-Attentive Structured Network
Bi-LSTM-CRF (Kumar et al., 2017) 79.2 Dialogue Act Sequence Labeling using Hierarchical encoder with CRF Link
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

Languages

The language supported is English.

Dataset Structure

Utterance are tagged with the SWBD-DAMSL DA.

Data Instances

An example from the dataset is:

{'dialogue_act_tag': 17, 'speaker': 0, 'utterance_text': 'Okay.'}

where 17 correspond to fo_o_fw_"_by_bc (Other)

Data Fields

speaker - Refers to the current speaker talking. It is used to detect when a speaker change occurs. There are two values for speaker: A and B. This does not mean we only have tow speakers in the whole datasets. It's only used to signal if next utterance is from same speaker or from next speaker. Since we encoded all labels A=0 and B=1.

utterance_text - Text that a speaker says.

dialogue_act_tag - Dialogue act label associated with the utterance_text. There are 41 dialogue act labels for this dataset. Each dialogue act label has a specific meaning:

| Int | Dialogue Act | Labels | |-- |------------------------------ |----------------- | | 0 | Statement-non-opinion | sd | | 1 | Acknowledge (Backchannel) | b | | 2 | Statement-opinion | sv | | 3 | Uninterpretable | % | | 4 | Agree/Accept | aa | | 5 | Appreciation | ba | | 6 | Yes-No-Question | qy | | 7 | Yes Answers | ny | | 8 | Conventional-closing | fc | | 9 | Wh-Question | qw | | 10 | No Answers | nn | | 11 | Response Acknowledgement | bk | | 12 | Hedge | h | | 13 | Declarative Yes-No-Question | qy^d | | 14 | Backchannel in Question Form | bh | | 15 | Quotation | ^q | | 16 | Summarize/Reformulate | bf | | 17 | Other | fo_o_fw_"_by_bc | | 18 | Affirmative Non-yes Answers | na | | 19 | Action-directive | ad | | 20 | Collaborative Completion | ^2 | | 21 | Repeat-phrase | b^m | | 22 | Open-Question | qo | | 23 | Rhetorical-Question | qh | | 24 | Hold Before Answer/Agreement | ^h | | 25 | Reject | ar | | 26 | Negative Non-no Answers | ng | | 27 | Signal-non-understanding | br | | 28 | Other Answers | no | | 29 | Conventional-opening | fp | | 30 | Or-Clause | qrr | | 31 | Dispreferred Answers | arp_nd | | 32 | 3rd-party-talk | t3 | | 33 | Offers, Options Commits | oo_co_cc | | 34 | Maybe/Accept-part | aap_am | | 35 | Downplayer | t1 | | 36 | Self-talk | bd | | 37 | Tag-Question | ^g | | 38 | Declarative Wh-Question | qw^d | | 39 | Apology | fa | | 40 | Thanking | ft |

Data Stats

Dialogue Act Labels Count % Train Count Train % Test Count Test % Val Count Val %
Statement-non-opinion sd 75136 37.62 72549 37.71 1317 32.30 1270 38.81
Acknowledge (Backchannel) b 38281 19.17 36950 19.21 764 18.73 567 17.33
Statement-opinion sv 26421 13.23 25087 13.04 718 17.61 616 18.83
Uninterpretable % 15195 7.61 14597 7.59 349 8.56 249 7.61
Agree/Accept aa 11123 5.57 10770 5.60 207 5.08 146 4.46
Appreciation ba 4757 2.38 4619 2.40 76 1.86 62 1.89
Yes-No-Question qy 4725 2.37 4594 2.39 84 2.06 47 1.44
Yes Answers ny 3030 1.52 2918 1.52 73 1.79 39 1.19
Conventional-closing fc 2581 1.29 2480 1.29 81 1.99 20 0.61
Wh-Question qw 1976 0.99 1896 0.99 55 1.35 25 0.76
No Answers nn 1374 0.69 1334 0.69 26 0.64 14 0.43
Response Acknowledgement bk 1306 0.65 1271 0.66 28 0.69 7 0.21
Hedge h 1226 0.61 1181 0.61 23 0.56 22 0.67
Declarative Yes-No-Question qy^d 1218 0.61 1167 0.61 36 0.88 15 0.46
Backchannel in Question Form bh 1053 0.53 1015 0.53 21 0.51 17 0.52
Quotation ^q 983 0.49 931 0.48 17 0.42 35 1.07
Summarize/Reformulate bf 952 0.48 905 0.47 23 0.56 24 0.73
Other fo_o_fw_"_by_bc 879 0.44 857 0.45 15 0.37 7 0.21
Affirmative Non-yes Answers na 847 0.42 831 0.43 10 0.25 6 0.18
Action-directive ad 745 0.37 712 0.37 27 0.66 6 0.18
Collaborative Completion ^2 723 0.36 690 0.36 19 0.47 14 0.43
Repeat-phrase b^m 687 0.34 655 0.34 21 0.51 11 0.34
Open-Question qo 656 0.33 631 0.33 16 0.39 9 0.28
Rhetorical-Question qh 575 0.29 554 0.29 12 0.29 9 0.28
Hold Before Answer/Agreement ^h 556 0.28 539 0.28 7 0.17 10 0.31
Reject ar 344 0.17 337 0.18 3 0.07 4 0.12
Negative Non-no Answers ng 302 0.15 290 0.15 6 0.15 6 0.18
Signal-non-understanding br 298 0.15 286 0.15 9 0.22 3 0.09
Other Answers no 284 0.14 277 0.14 6 0.15 1 0.03
Conventional-opening fp 225 0.11 220 0.11 5 0.12 0 0.00
Or-Clause qrr 209 0.10 206 0.11 2 0.05 1 0.03
Dispreferred Answers arp_nd 207 0.10 204 0.11 3 0.07 0 0.00
3rd-party-talk t3 117 0.06 115 0.06 0 0.00 2 0.06
Offers, Options Commits oo_co_cc 110 0.06 109 0.06 0 0.00 1 0.03
Maybe/Accept-part aap_am 104 0.05 97 0.05 7 0.17 0 0.00
Downplayer t1 103 0.05 102 0.05 1 0.02 0 0.00
Self-talk bd 103 0.05 100 0.05 1 0.02 2 0.06
Tag-Question ^g 92 0.05 92 0.05 0 0.00 0 0.00
Declarative Wh-Question qw^d 80 0.04 79 0.04 1 0.02 0 0.00
Apology fa 79 0.04 76 0.04 2 0.05 1 0.03
Thanking ft 78 0.04 67 0.03 7 0.17 4 0.12

Label Frequencies

Data Splits

he data is split into the original training and test sets suggested by the authors (1115 training and 19 test). The remaining 21 dialogues have been used as a validation set.

Dataset Creation

Curation Rationale

[More Information Needed]

Source Data

Initial Data Collection and Normalization

  • Total number of utterances: 199740
  • Maximum utterance length: 133
  • Mean utterance length: 9.6
  • Total number of dialogues: 1155
  • Maximum dialogue length: 457
  • Mean dialogue length: 172.9
  • Vocabulary size: 22301
  • Number of labels: 41
  • Number of dialogue in train set: 1115
  • Maximum length of dialogue in train set: 457
  • Number of dialogue in test set: 19
  • Maximum length of dialogue in test set: 330
  • Number of dialogue in val set: 21
  • Maximum length of dialogue in val set: 299

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, Stanford Linguistics.

Licensing Information

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License.

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}}