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A Study on Dialog Act Recognition using Character-Level Tokenization

Paper · 2018 · Eugenio Ribeiro; Ricardo Ribeiro; and David Martins de Matos

Summary

The paper asks whether dialog act recognition, which identifies the intention behind an utterance, gains from looking below the word level. Most neural systems treat a segment as a sequence of words. The authors argue that affixes, punctuation and similar sub-word cues also signal intention. They took the word-level CNN architecture of Liu et al., which runs parallel convolutions with different window widths, applies max pooling, reduces dimensionality and ends in a softmax, and fed it characters instead of words. Experiments covered two contrasting corpora: Switchboard (English, open-domain human-human talk) and DIHANA (Spanish, users talking to a Wizard of Oz train-information system). They varied the window sizes, and tested capitalization, punctuation and lemmatization. Character-level models matched or beat the word-level baselines. Adding word-level representations improved results further, which shows the two views carry complementary information. With context features included, the combined model exceeded the earlier state of the art on Switchboard and came close to perfect on DIHANA. This suggests sub-word cues for intention hold across languages and domains.

Key points

  • Data: Switchboard has 1,155 dialogs, 223,606 segments and 42 labels. DIHANA has 900 dialogs and 23,542 segments, scored on the 11 domain-independent Level 1 tags, of which only 5 are shared by user and system turns.
  • The best character setup used three parallel windows of 3, 5 and 7 characters. It reached .7208 accuracy on the Switchboard test set and .9244 on DIHANA, beating any single window.
  • Keeping punctuation added about 1 percentage point (Switchboard test .7317). Lemmatizing the input lowered accuracy (.7140), but only slightly.
  • Capitalization made no difference on Switchboard. On DIHANA it helped (.9425), because system turns lack mid-segment capitals, so capitalization revealed which side was speaking.
  • Combining character and word representations gave .7401 on Switchboard and .9568 on DIHANA. Adding context raised this to .7901 and .9910, about 0.7 points above the prior Switchboard best.

Interesting ideas

  • I1. Running several character windows of different widths in parallel lets one model pick up short affixes, whole stems and cross-word fragments at once, without choosing a single n-gram size.
  • I2. Feeding the model lemmatized input is a cheap ablation that measures how much of the signal lives in inflection rather than in the lexeme.
  • I3. When capitalization unexpectedly helped, the authors traced the gain to a transcription artifact that exposed who was speaking, rather than accepting it as linguistic signal.
  • I4. Punctuation works both as a direct cue to intention (a question mark) and as an indirect structural one (commas organizing the segment).
  • I5. Character-level and word-level representations proved complementary, so merging them beat either one alone.

For Mova

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

  • I1 (tokens): Char windows of 3/5/7 could supply features for tagging and lemmatizing unknown or misspelled English words, with each window's contribution kept inspectable.
  • I2 (learn): The same ablation could act as a negative control when evaluating whether a parser or speech-act classifier really uses morphology.
  • I3 (learn): Evaluation could routinely check whether surprising gains come from formatting quirks of a corpus source, especially when silver data mixes treebanks with different conventions.
  • I4 (frames): The speech-act detector in prag should treat terminal and internal punctuation as explicit, explainable evidence alongside the UD tree.
  • I5 (frames): Speech-act and irony decisions could combine lexical/tree features with a small character-level cue channel, recording which channel drove each decision.

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

 Institutions

🔬Instituto de Engenharia de Sistemas e Computadores Investigação e DesenvolvimentoLisbon · Portugal21 works · 3,723 citations🎓Instituto Superior TécnicoLisbon · Portugal28 works · 3,944 citations🎓University of LisbonLisbon · Portugal20 works · 3,477 citations🎓Iscte – Instituto Universitário de LisboaLisbon · Portugal5 works · 34 citations

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 9,086 worksArtificial Intelligence · 8,889 worksArts and Humanities · 9,296 worksLanguage and Linguistics · 9,206 works

🧭 Topics 3Research subjects and languages the work is about.Dialogue systems · 401 worksTokenization & segmentation · 77 worksSpeech act theory · 160 works

Cite

Cite as Eugenio Ribeiro; Ricardo Ribeiro; and David Martins de Matos. 2018. A Study on Dialog Act Recognition using Character-Level Tokenization. arXiv:1805.07231.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.1805.07231 · arXiv 1805.07231

@misc{ribeiro2018study,
  title = {{A Study on Dialog Act Recognition using Character-Level Tokenization}},
  author = {Eugenio Ribeiro; Ricardo Ribeiro; and David Martins {de Matos}},
  year = {2018},
  doi = {10.48550/arxiv.1805.07231},
  eprint = {1805.07231},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.1805.07231}
}

References

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