Dataset Card for SwDA

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
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
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	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:

{'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 repo: The same training and test splits as used by Stolcke et al. (2000). 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.

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, 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}}

Contributions

Thanks to @gmihaila for adding this dataset.