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# Dialogue Act Modeling for Automatic Tagging and Recognition of Conversational Speech

**Authors:** Andreas Stolcke, Klaus Ries, Noah Coccaro, Elizabeth Shriberg, Rebecca Bates, [Daniel Jurafsky](https://md.mova.elamur.ai/free/en/docs/papers/authors/daniel-jurafsky/), Paul Taylor, Rachel Martin, Carol Van Ess-Dykema, Marie Meteer · **Year:** 2000 · **Venue:** Computational Linguistics 26(3) **Link:** [aclanthology.org/J00-3003](https://aclanthology.org/J00-3003/) · **Licence:** [CC BY-NC-SA 3.0](https://creativecommons.org/licenses/by-nc-sa/3.0/) (ACL Anthology, before 2016) — summary only

[**339** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/prag-2000-stolcke-dialogue-act-modeling-automatic-tagging/) of the 140673 papers whose bibliographies we read[licence](https://creativecommons.org/licenses/by-nc-sa/3.0/)

- [Publisher's page](https://aclanthology.org/J00-3003/)
- [PDF](https://aclanthology.org/J00-3003.pdf)
- [arXiv cs/0006023](https://arxiv.org/abs/cs/0006023)

**Authors (10)**

1. Andreas Stolcke
2. Klaus Ries
3. Noah Coccaro
4. Elizabeth Shriberg
5. Rebecca Bates
6. Daniel Jurafsky
7. Paul Taylor
8. Rachel Martin
9. Carol Van Ess-Dykema
10. Marie Meteer

Citations and author metrics from OpenAlex (); authors with the same name are not always told apart.

## What it is about

The paper labels each utterance of spontaneous telephone speech with one of 42 dialogue acts (statement, opinion, yes-no question, backchannel, agreement, answer and others), and asks how much the order of the acts helps. The data are 1,155 Switchboard conversations, hand-labelled by eight graduate students (about 205,000 utterances, kappa 0.80), with 1,115 conversations for training and 19 for test. The model is a hidden Markov model: the acts are the hidden states, the words (true or recognised) and the prosody give the observations, an act n-gram gives the transitions, and forward-backward gives each utterance's posterior. With true words, accuracy rises from 54.3% with no act grammar to 70.6% with a bigram and 71.0% with a trigram, against 35% chance and 84% human agreement; with recognised words the best is 64.8%. Prosody alone reaches 49.7% with a bigram, and adding it to the recognised words gains only 0.7 points. Act-specific language models in the recogniser cut word error only slightly, and even perfect act labels give 2.2% relative.

## Ideas

- I1. Test whether an act grammar adds anything beyond the previous-act feature. The SLM's act labeller (`prag/src/feats.rs`, `prag/src/agree.rs`) already sees the act of the previous sentence. On school dialogues with gold acts, add an act-bigram prior with forward-backward over each conversation, and compare it with the current labeller on held-out conversations by two-fold `cv`. The paper's gain over no grammar is large, but the trigram adds only 0.4 points, so a higher order stays out unless `cv` asks for it.
- I2. Shallow trees for the confusable pairs, as the paper does for backchannel against agreement (its Figure 3). The labeller's largest confusions (assert against narrate, from the teacher-change counts in [31. Speech acts](https://md.mova.elamur.ai/free/en/docs/languages/en/31-speech-acts/) §9) can get a depth-3 tree over the existing text features (length, the previous act, the question mark, the first word). Each label is printed with the path that produced it. Keep the tree only if it beats the current labeller on those pairs by `cv`; run twice and compare the outputs.
- I3. A negative control for the act sequence: shuffle the acts within each conversation and rerun the bigram. If accuracy does not fall back to the no-grammar level, the gain comes from something other than order, and the claim is not made.
- I4. Adjacency checks in the dialogue layer (`world/src/talk.rs`). The paper's bigram counts give expected next acts: about 30% of yes-no questions are followed by a yes answer and 14% by a no answer, commands are followed by agreement in 23% of cases, and statements draw backchannels in 26%. Log each turn that breaks a high-probability pair together with the rule it broke. This is a transparent check, not a learned answer.

## For Mova

The chapters are [31. Speech acts](https://md.mova.elamur.ai/free/en/docs/languages/en/31-speech-acts/) (§7 schemes, §9 how Mova labels acts, §10 prosody absent from text) and [38. Dialogue, discourse markers and reported speech](https://md.mova.elamur.ai/free/en/docs/languages/en/38-dialogue-and-discourse-markers/). The code is `prag/src/feats.rs`, `prag/src/agree.rs`, `prag/src/schema.rs` (the act enum) and `world/src/talk.rs`. Measures: two-fold `cv` on school dialogues, two identical runs, and the shuffled-order control in I3. Licence: the paper is CC BY-NC-SA, so this card is a summary only. The Switchboard transcripts are distributed by the LDC, and the paper does not state their licence, so any Switchboard data is for tests only. The 42-label set is a design reference; Mova's own act set is what goes into a release. The prosodic ideas do not apply to text, so I2 uses text features only.

Topics: [Speech act theory](https://md.mova.elamur.ai/free/en/docs/papers/topics/speech-act-theory/)

## Cite

The paper itself, not this card. BibTeX from the library's record of the work. [arXiv cs/0006023](https://arxiv.org/abs/cs/0006023)

```
@misc{stolcke2000dialogue,
  title = {{Dialogue Act Modeling for Automatic Tagging and Recognition of Conversational Speech}},
  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 Ess-Dykema, Carol Van and Meteer, Marie},
  year = {2000},
  eprint = {cs/0006023},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/cs/0006023}
}
```

- [← previous Resolving Discourse Deictic Anaphora in Dialogues](https://md.mova.elamur.ai/free/en/docs/papers/prag-1999-eckert-resolving-discourse-deictic-anaphora-dialogues/)
- [next → Sarcasm as Contrast between a Positive Sentiment and Negative Situation](https://md.mova.elamur.ai/free/en/docs/papers/prag-2013-riloff-sarcasm-as-contrast-between-positive/)
- [↑ back to English](https://md.mova.elamur.ai/free/en/docs/papers/en/)

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