Datasets:
swda

Task Categories: text-classification
Languages: en
Multilinguality: monolingual
Size Categories: 100K<n<1M
Language Creators: found
Annotations Creators: found
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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.

Models trained or fine-tuned on swda

None yet