---
title: "The Influence of Context on Dialogue Act Recognition (2015)"
description: "The paper asks how much the surrounding dialog helps a classifier decide which dialog act a… 46 references, cited by 15 papers here."
authors: ["Eugenio Ribeiro", "Ricardo Ribeiro", "David Martins de Matos"]
year: 2015
venue: "arXiv:1506.00839"
source: "https://arxiv.org/abs/1506.00839"
doi: "10.48550/arxiv.1506.00839"
arxiv: "1506.00839"
licence: "arXiv non-exclusive distribution licence (read only, no redistribution)"
licence_url: "http://arxiv.org/licenses/nonexclusive-distrib/1.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/ribeiro-2015-influence-context-dialogue-act-recognition/"
---
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# The Influence of Context on Dialogue Act Recognition

**Authors:** [Eugenio Ribeiro](https://md.research.elamur.ai/free/en/authors/eugenio-ribeiro/), [Ricardo Ribeiro](https://md.research.elamur.ai/free/en/authors/ricardo-ribeiro/) and [David Martins de Matos](https://md.research.elamur.ai/free/en/authors/david-martins-de-matos/) · **Year:** 2015 · **Venue:** arXiv:1506.00839 · **Licence:** [arXiv non-exclusive distribution licence (read only, no redistribution)](http://arxiv.org/licenses/nonexclusive-distrib/1.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/1506.00839) · [PDF](https://arxiv.org/pdf/1506.00839) · [DOI 10.48550/arxiv.1506.00839](https://doi.org/10.48550/arxiv.1506.00839) · [arXiv 1506.00839](https://arxiv.org/abs/1506.00839) · [OpenAlex](https://openalex.org/W2404576907)

**Citations:** 17 citations (OpenAlex, 2026-10-14) · [15](https://md.research.elamur.ai/free/en/papers/ribeiro-2015-influence-context-dialogue-act-recognition/cited-by/) in our library, of the 141,840 papers whose bibliographies we read

## Summary
The paper asks how much the surrounding dialog helps a classifier decide which dialog act a segment performs, and how far back that help extends. The authors used the large Switchboard corpus and data following the ISO 24617-2 standard. The standard-annotated data came from the Tilburg DialogBank and from part of the LEGO (Let's Go) corpus, whose original labels were converted into ISO communicative functions. The classifier was an SVM. Its base features were n-grams, punctuation and wh-words. Context came from one to five earlier segments and was encoded in three ways: raw n-grams mixed with the current ones, n-grams tagged with their distance, or the dialog act labels of earlier segments. Only past information was used, so the setup suits live systems.

Mixing context words in without marking them usually hurt accuracy. Distance-tagged n-grams helped a lot, but only for the nearest segment. Previous act labels gave steady gains that fade with distance. The Switchboard results beat earlier published numbers, and the ISO results serve as a baseline for later work.

## Key points
- **Datasets:** Switchboard (about 224k segments, 44-tag set, with 41–44 tag variants in the literature), Tilburg DialogBank, and LEGO mapped onto ISO 24617-2 communicative functions.
- **Untagged n-gram context:** this was generally harmful. The only exception was the single preceding segment in LEGO, which the authors attribute to the rigid wording of system turns.
- **Distance-tagged n-grams:** these gave a large improvement from the first preceding segment and nothing visible beyond it.
- **Dialog act label context:** this improved accuracy gradually, with influence shrinking roughly exponentially with distance and leveling off near the third segment. The same pattern appeared with automatically predicted labels, not just gold ones.
- **ISO dimensions:** adding information from dimensions other than Task gave at most about 2 percentage points, mostly not significant. For comparison, an earlier HMM baseline (Stolcke et al.) reached 71.0% on Switchboard.

## Interesting ideas
- Context features should be kept separate from current-segment features, either by tagging each with its distance or by using a different feature type, because blending them adds noise instead of signal.
- Compact summaries of earlier turns (their act labels) carried context better and more stably than their raw words, and about three turns back was enough.
- The authors checked that the context effect still held when gold labels were swapped for the system's own predictions, a realistic test of error propagation.
- Converting a domain-specific annotation scheme into a shared standard (LEGO into ISO 24617-2) produced extra comparable data and a reusable baseline.
- The authors restricted themselves to past context only, so the setup reflects what a live system actually knows at decision time.

## For Mova

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

- **I1** (frames): when crate prag classifies a speech act, features drawn from earlier sentences should carry a distance marker rather than being merged into the current sentence's evidence.
- **I2** (frames): the speech-act layer can condition on the act labels of the last two or three utterances instead of their full text, which keeps the decision small and easy to explain.
- **I3** (learn): any feature fed from an upstream Mova stage should be evaluated with predicted inputs as well as gold, so pipeline errors are measured rather than hidden.
- **I4** (learn): mapping existing dialog or intent corpora onto one standard act inventory could provide additional silver training and evaluation data for the speech-act component.
- **I5** (answer): readers that respond turn by turn should be tested without look-ahead, so reported accuracy matches real interactive use.

*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: Eugenio Ribeiro, Ricardo Ribeiro and David Martins de Matos. 2015. The Influence of Context on Dialogue Act Recognition. arXiv:1506.00839.

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

```bibtex
@misc{ribeiro2015influence,
  title = {{The Influence of Context on Dialogue Act Recognition}},
  author = {Eugenio Ribeiro, Ricardo Ribeiro and David Martins {de Matos}},
  year = {2015},
  doi = {10.48550/arxiv.1506.00839},
  eprint = {1506.00839},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.1506.00839}
}
```

- [← previous PDTB Discourse Parsing as a Tagging Task: The Two Taggers Approach](https://md.research.elamur.ai/free/en/papers/biran-2015-pdtb-discourse-parsing-as-tagging/)
- [next → Factors Influencing the Implicitation of Discourse Relations across Languages](https://md.research.elamur.ai/free/en/papers/hoek-2015-factors-influencing-implicitation-discourse-relations/)
- [↑ back to papers of 2015](https://md.research.elamur.ai/free/en/papers/2015/page/2/)

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46. Wright, Helen. 1998. Automatic Utterance Type Detection Using Suprasegmental Features. In ICSLP. ISCA. Book Reviews [find](https://scholar.google.com/scholar?q=Wright%2C+Helen%2E+1998%2E+Automatic+Utterance+Type+Detection+Using+Suprasegmental+Features%2E+In+ICSLP%2E+ISCA%2E+Book+Reviews)

*Updated 24 October 2026*