Datasets:

cgpotts
/

swda

	---
	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-description)
	- [Dataset Summary](#dataset-summary)
	- [Supported Tasks](#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)

	## Dataset Description

	- Homepage: [The Switchboard Dialog Act Corpus](http://compprag.christopherpotts.net/swda.html)
	- Repository: [NathanDuran/Switchboard-Corpus](https://github.com/NathanDuran/Switchboard-Corpus)
	- 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 \|
	\| ------------- \| :-----:\| --- \| --- \|
	\| 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) \| \|
	\| 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:

	`{'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](https://raw.githubusercontent.com/NathanDuran/Switchboard-Corpus/master/swda_data/metadata/Swda%20Label%20Frequency%20Distributions.png)

	### 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](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}}
	```