---
title: "Training a Dialogue Act Tagger for Human-human and"
description: "Hand-labelling dialogues with dialogue act tags is costly. 98.5% accuracy. 19 references, cited by 5 papers here."
authors: ["Rashmi Prasad", "Marilyn Walker"]
year: 2002
venue: "Proceedings of the Third SIGdial Workshop on Discourse and Dialogue"
source: "https://aclanthology.org/W02-0221/"
doi: "10.3115/1118121.1118142"
licence: "CC BY-NC-SA 3.0 (ACL Anthology, before 2016)"
licence_url: "https://creativecommons.org/licenses/by-nc-sa/3.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/prasad-2002-training-dialogue-act-tagger-human/"
---
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# Training a Dialogue Act Tagger for Human-human and Human-computer Travel dialogues

**Authors:** [Rashmi Prasad](https://md.research.elamur.ai/free/en/authors/rashmi-prasad/) and [Marilyn Walker](https://md.research.elamur.ai/free/en/authors/marilyn-walker/) · **Year:** 2002 · **Venue:** [Proceedings of the Third SIGdial Workshop on Discourse and Dialogue](https://md.research.elamur.ai/free/en/venues/sigdial/) ([SIGDIAL 2002](https://md.research.elamur.ai/free/en/venues/sigdial/2002/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY-NC-SA 3.0 (ACL Anthology, before 2016)](https://creativecommons.org/licenses/by-nc-sa/3.0/)

**Links:** [Publisher's page](https://aclanthology.org/W02-0221/) · [PDF](https://aclanthology.org/W02-0221.pdf) · [DOI 10.3115/1118121.1118142](https://doi.org/10.3115/1118121.1118142) · [OpenAlex](https://openalex.org/W2096025626)

**Citations:** 17 citations (OpenAlex, 2026-10-14) · [5](https://md.research.elamur.ai/free/en/papers/prasad-2002-training-dialogue-act-tagger-human/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Hand-labelling dialogues with dialogue act tags is costly. Earlier, the DATE (Dialogue Act Tagging for Evaluation) scheme was applied to DARPA Communicator data with a pattern matcher built on a large hand-labelled pattern database. That matcher broke down when system prompts changed: it labelled only about 60% of the October-2001 data. Prasad and Walker try to automate DATE tagging of the information provider's turns, meaning the system in human-computer (HC) dialogues and the travel agent in human-human (HH) dialogues. They train the rule learner RIPPER on the June-2000 and October-2001 Communicator corpora and the CMU HH travel corpus, using textual and contextual features, and test several mixes of these sources. Within HC data, accuracy is very high. A tagger trained on older systems drops sharply on newer ones, but a modest amount of new labelled data recovers most of the loss. When HH labels are scarce, adding HC data raises HH accuracy. This suggests cheap machine-generated dialogues can help bootstrap annotation of harder human conversation.

## Key points
- **Corpora:**
  - June-2000: 663 dialogues, 9 systems, 22,930 system utterances.
  - October-2001: 1,252 dialogues, 8 systems, 69,766 utterances.
  - CMU-corpus: 38 HH dialogues, 1,062 agent utterances, segmented so that each unit carries one dialogue act.
- **DATE dimensions:** DATE labels each utterance on three orthogonal axes: SPEECH ACT, CONVERSATIONAL-DOMAIN and TASK-SUBTASK. The first two are meant to be domain-general; the third is domain- and system-specific.
- **HC results:**
  - Training and testing on June-2000 gives 98.5% accuracy.
  - The same tagger reaches only 71.8% on October-2001.
  - Adding 2,000 October-2001 utterances to training raises this to 93.8%.
- **HH results (speech-act tags):**
  - HC-only training gives 36.7%, against a 28% majority baseline.
  - 305 HH training utterances give 48.75%; adding the HC data raises this to 55.48%.
  - 20-fold cross-validation on the CMU-corpus reaches 76.56%.
- **Why RIPPER:** it accepts textual (set-valued word) features and produces ordered, readable if-then rules.

## Interesting ideas
- Pair a large, uniform, easy source (machine dialogues) with a few labels from the hard target (human dialogues); the easy data helps most when target labels are scarce and its benefit fades as target data grows.
- Learn a classifier that outputs ordered if-then rules over word and context features, so each decision can be traced to the rule that fired and the features it used.
- Test a tagger trained on an older version of a source on a later version, to expose drift. A brittle exact-pattern approach lost much of its coverage when prompts changed slightly, while a learned model plus a little fresh data recovered.
- Error analysis showed that the same act surfaces differently across settings: human agents give information in small pieces that look like the system's implicit confirmations.
- Tag dialogue acts on separate orthogonal dimensions, keeping the general axes (speech act, conversational domain) apart from the domain-specific task axis.

## For Mova

How these ideas could serve Mova, our small open language model — its files, stages and tests:

- **I1** (learn): Mix plentiful silver data from simple, regular texts with small gold sets from harder genres, and measure how the gain changes as the gold set grows.
- **I2** (frames): Speech-act classification in prag could use induced, human-readable rules rather than opaque scores, which keeps each step explainable.
- **I3** (learn): Add drift checks that train on one time slice or genre and evaluate on another, flagging rule sets that only match exact surface strings.
- **I4** (frames): Speech-act rules should rely on dialogue context and function, not just utterance length or form, so that short informative turns are not mistaken for confirmations.
- **I5** (frames): Keep the speech-act label separate from the task or frame slot, so the general part carries over to new domains while only the task layer needs domain knowledge.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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## Cite

Cite as: Rashmi Prasad and Marilyn Walker. 2002. Training a Dialogue Act Tagger for Human-human and Human-computer Travel dialogues. Proceedings of the Third SIGdial Workshop on Discourse and Dialogue.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{prasad2002training,
  title = {{Training a Dialogue Act Tagger for Human-human and Human-computer Travel dialogues}},
  author = {Rashmi Prasad and Marilyn Walker},
  booktitle = {Proceedings of the Third SIGdial Workshop on Discourse and Dialogue},
  year = {2002},
  doi = {10.3115/1118121.1118142},
  url = {https://doi.org/10.3115/1118121.1118142}
}
```

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## References

19 references, 3 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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*Updated 2 November 2026*