---
title: "Deep Dialog Act Recognition using Multiple Token, Segment, and"
description: "The authors break the problem into three representation levels and test options at each one. 1.38 points. 63 references, cited by 8 papers here."
authors: ["Eugenio Ribeiro; Ricardo Ribeiro;", "David Martins de Matos"]
year: 2018
venue: "arXiv:1807.08587"
source: "https://arxiv.org/abs/1807.08587"
doi: "10.48550/arxiv.1807.08587"
arxiv: "1807.08587"
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-2018-deep-dialog-act-recognition-using/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/ribeiro-2018-deep-dialog-act-recognition-using/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2018](https://md.research.elamur.ai/free/en/papers/2018/page/5/) / Deep Dialog Act Recognition using Multiple Token, Segment, and Context Information Representations

# Deep Dialog Act Recognition using Multiple Token, Segment, and Context Information Representations

**Authors:** [Eugenio Ribeiro; Ricardo Ribeiro;](https://md.research.elamur.ai/free/en/authors/eugenio-ribeiro/) and [David Martins de Matos](https://md.research.elamur.ai/free/en/authors/david-martins-de-matos/) · **Year:** 2018 · **Venue:** [arXiv:1807.08587](https://md.research.elamur.ai/free/en/venues/journal-of-artificial-intelligence-research/) ([Journal of Artificial Intelligence Research 2018](https://md.research.elamur.ai/free/en/venues/journal-of-artificial-intelligence-research/2018/)) · **Publisher:** [AI Access Foundation](https://md.research.elamur.ai/free/en/publishers/ai-access-foundation/) · **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/1807.08587) · [PDF](https://arxiv.org/pdf/1807.08587) · [DOI 10.48550/arxiv.1807.08587](https://doi.org/10.48550/arxiv.1807.08587) · [arXiv 1807.08587](https://arxiv.org/abs/1807.08587) · [OpenAlex](https://openalex.org/W2883439102)

**Citations:** 10 citations (OpenAlex, 2026-10-14) · [8](https://md.research.elamur.ai/free/en/papers/ribeiro-2018-deep-dialog-act-recognition-using/cited-by/) in our library, of the 141,840 papers whose bibliographies we read

## Summary
The paper tackles automatic dialog act recognition: deciding what a speaker intends with each segment of a dialog, such as a question, a statement or an agreement. The authors break the problem into three representation levels and test options at each one. At the token level they combine word-level input (classic and contextualized BERT embeddings), character-level input aimed at morphological cues, and a functional abstraction that hides words and keeps segment structure. At the segment level they compare RNN- and CNN-based encoders. At the context level they encode the labels of earlier segments, condensed by a recurrent summarizer, together with turn-taking history. Experiments on SwDA and MRDA, including replications of earlier state-of-the-art systems on the same splits, show the combined system beating prior work. The margin is about 2 points on SwDA and over 10 points on MRDA. When future segments are also visible, as they are to a human annotator, the system roughly matches human agreement on SwDA and exceeds it on MRDA. The work shows that well-chosen inputs and context matter more than elaborate segment encoders.

## Key points
- Evaluated on two English corpora: Switchboard Dialog Act Corpus (SwDA) and ICSI Meeting Recorder Dialog Act Corpus (MRDA).
- With BERT word embeddings, replacing the segment encoder with plain max pooling cost under one percentage point on SwDA and almost nothing on MRDA. The authors kept a CNN encoder because it is cheaper.
- The character-level CNN uses windows of 3, 5 and 7 characters, aimed at affixes and lemmas.
- Context works best as a recurrent summary of earlier segments' predicted labels. On SwDA, the gap between gold-label context and the classifier's own predictions was only 1.38 points. For turn-taking, a flat flag over the three previous segments worked best.
- With past and future context, the system passed the 84% inter-annotator agreement on the SwDA validation set but stayed 1.66 points below it on the test set.

## Interesting ideas
- Feed context to the model as the *labels* of earlier units, condensed into a running summary, instead of their raw words; this keeps context compact and order-aware.
- Measure the upper bound by giving the model gold context, then compare it with self-predicted context to see how much errors propagate.
- Choose character window sizes on purpose, matching typical affix lengths, rather than tuning them blindly, so the morphology cues are interpretable.
- Abstract words away into functional or syntactic units so the classifier attends to segment structure, which signals acts like questions or commands.
- Ablate down to the simplest encoder (max pooling) and replicate competing systems on identical splits before claiming gains.

## For Mova

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

- **I1** (frames): the speech-act layer in prag could use the sequence of already-assigned speech acts from earlier sentences as an explicit, explainable feature for labeling the current one.
- **I2** (learn): any stage that consumes its own earlier outputs (coreference, event order) can report a gold-vs-predicted gap as a standard diagnostic of error cascades.
- **I3** (tokens): the en tagger could expose fixed prefix/suffix windows as named features, making it explainable why a word got a given tag or mood.
- **I4** (frames): speech-act detection can key on UD tree shapes (aux inversion, imperative root, wh-word position) rather than on lexical items.
- **I5** (learn): every new parser or reader component should be benchmarked against a trivial baseline and re-run rivals on the same data, as a built-in negative control.

*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 Desenvolvimento](https://md.research.elamur.ai/free/en/labs/instituto-de-engenharia-de-sistemas-e-computadores-investigacao-e/) — Research institute, Lisbon, Portugal · 21 works · 3,723 citations
- [Instituto Superior Técnico](https://md.research.elamur.ai/free/en/universities/instituto-superior-tecnico/) — University, Lisbon, Portugal · 28 works · 3,944 citations
- [Iscte – Instituto Universitário de Lisboa](https://md.research.elamur.ai/free/en/universities/iscte-instituto-universitario-de-lisboa/) — University, Lisbon, Portugal · 5 works · 34 citations

📚 Shelves (4): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 9,086 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 8,889 works · [🎭 Arts and Humanities](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · 9,296 works · [🗣️ Language and Linguistics](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · 9,206 works

🧭 Topics (2): [🗨️ Dialogue systems](https://md.research.elamur.ai/free/en/topics/dialogue-systems/) · 401 works · [💬 Speech act theory](https://md.research.elamur.ai/free/en/topics/speech-act-theory/) · 160 works

## Cite

Cite as: Eugenio Ribeiro; Ricardo Ribeiro; and David Martins de Matos. 2018. Deep Dialog Act Recognition using Multiple Token, Segment, and Context Information Representations. arXiv:1807.08587.

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

```bibtex
@misc{ribeiro2018deep,
  title = {{Deep Dialog Act Recognition using Multiple Token, Segment, and Context Information Representations}},
  author = {Eugenio Ribeiro; Ricardo Ribeiro; and David Martins {de Matos}},
  year = {2018},
  doi = {10.48550/arxiv.1807.08587},
  eprint = {1807.08587},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.1807.08587}
}
```

- [← previous Cross-Document, Cross-Language Event Coreference Annotation Using Event Hoppers](https://md.research.elamur.ai/free/en/papers/song-2018-cross-document-cross-language-event/)
- [next → Finding Variants for Construction-Based Dialectometry: A Corpus-Based Approach to Regional CxGs](https://md.research.elamur.ai/free/en/papers/dunn-2021-finding-variants-construction-based-dialectometry/)
- [↑ back to papers of 2018](https://md.research.elamur.ai/free/en/papers/2018/page/5/)

## References

63 references, 29 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Abadi, M., et al. (2015). TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. [https://www.tensorflow.org/](https://www.tensorflow.org/). [↗](https://www.tensorflow.org/)
2. Alexandersson, J., Buschbeck-Wolf, B., Fujinami, T., Kipp, M., Koch, S., Maier, E., Reithinger, N., Schmitz, B., & Siegel, M. (1998). Dialogue Acts in VERBMOBIL-2 Second Edition. Tech. rep., DFKI. [find](https://scholar.google.com/scholar?q=Alexandersson%2C+J%2E%2C+Buschbeck%2DWolf%2C+B%2E%2C+Fujinami%2C+T%2E%2C+Kipp%2C+M%2E%2C+Koch%2C+S%2E%2C+Maier%2C+E%2E%2C+Reithinger%2C+N%2E%2C+Schmitz%2C+B%2E%2C+%26+Siegel%2C+M%2E+%281998%29%2E+Dialogue+Acts+in+VERBMOBIL%2D2+Second+Edition%2E+Tech%2E+rep%2E%2C+DFKI%2E)
3. Anderson, A. H., Bader, M., Bard, E. G., Boyle, E., Doherty, G., Garrod, S., Isard, S., Kowtko, J., McAllister, J., Miller, J., et al. (1991). The HCRC Map Task Corpus. Language and Speech, 34 (4), 351–366. [↗](https://doi.org/10.1177/002383099103400404)
4. Ang, J., Liu, Y., & Shriberg, E. (2005). Automatic Dialog Act Segmentation and Classification in Multiparty Meetings. In ICASSP, Vol. 1, pp. 1061–1064. [find](https://scholar.google.com/scholar?q=Ang%2C+J%2E%2C+Liu%2C+Y%2E%2C+%26+Shriberg%2C+E%2E+%282005%29%2E+Automatic+Dialog+Act+Segmentation+and+Classification+in+Multiparty+Meetings%2E+In+ICASSP%2C+Vol%2E+1%2C+pp%2E+1061%E2%80%931064%2E)
5. Baum, L. E., & Petrie, T. (1966). Statistical Inference For Probabilistic Functions Of Finite State Markov Chains. Annals of Mathematical Statistics, 37 (6), 1554–1563. [↗](https://doi.org/10.1214/aoms/1177699147)
6. Benedı́, J.-M., Lleida, E., Varona, A., Castro, M.-J., Galiano, I., Justo, R., de Letona, I. L., & Miguel, A. (2006). Design and Acquisition of a Telephone Spontaneous Speech Dialogue Corpus in Spanish: DIHANA. In LREC, pp. 1636–1639. [find](https://scholar.google.com/scholar?q=Bened%C4%B1%CC%81%2C+J%2E%2DM%2E%2C+Lleida%2C+E%2E%2C+Varona%2C+A%2E%2C+Castro%2C+M%2E%2DJ%2E%2C+Galiano%2C+I%2E%2C+Justo%2C+R%2E%2C+de+Letona%2C+I%2E+L%2E%2C+%26+Miguel%2C+A%2E+%282006%29%2E+Design+and+Acquisition+of+a+Telephone+Spontaneous+Speech+Dialogue+Corpus+in+Spanis)
7. Bojanowski, P., Grave, E., Joulin, A., & Mikolov, T. (2017). [Enriching Word Vectors with Subword Information](https://md.research.elamur.ai/free/en/papers/bojanowski-2017-enriching-word-vectors-subword-information/). Transactions of the Association for Computational Linguistics, 5, 135–146. [↗](https://doi.org/10.1162/tacl_a_00051)
8. Camacho-Collados, J., & Pilehvar, M. T. (2018). From Word to Sense Embeddings: A Survey on Vector Representations of Meaning. Journal of Artificial Intelligence Research, 63, 743–788. [↗](https://doi.org/10.1613/jair.1.11259)
9. Carletta, J. (1996). [Assessing Agreement on Classification Tasks: The Kappa Statistic](https://md.research.elamur.ai/free/en/papers/carletta-1996-assessing-agreement-classification-tasks-kappa/). Computational Linguistics, 22 (2), 249–254. [↗](https://doi.org/10.48550/arxiv.cmp-lg/9602004)
10. Chollet, F., et al. (2015). Keras: The Python Deep Learning Library. [https://keras.io/](https://keras.io/). [↗](https://keras.io/)
11. Chung, J., Gülcehre, C., Cho, K., & Bengio, Y. (2014). Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling. In NIPS Deep Learning and Representation Learning Workshop. [find](https://scholar.google.com/scholar?q=Chung%2C+J%2E%2C+Gu%CC%88lcehre%2C+C%2E%2C+Cho%2C+K%2E%2C+%26+Bengio%2C+Y%2E+%282014%29%2E+Empirical+Evaluation+of+Gated+Recurrent+Neural+Networks+on+Sequence+Modeling%2E+In+NIPS+Deep+Learning+and+Representation+Learning+Workshop%2E)
12. Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., & Kuksa, P. (2011). [Natural Language Processing (Almost) from Scratch](https://md.research.elamur.ai/free/en/papers/collobert-2011-natural-language-processing-almost-scratch/). Journal of Machine Learning Research, 12, 2493–2537.
13. Conneau, A., Schwenk, H., Barrault, L., & Lecun, Y. (2017). [Very Deep Convolutional Networks for Text Classification](https://md.research.elamur.ai/free/en/papers/conneau-2017-very-deep-convolutional-networks-text/). In EACL, Vol. 1, pp. 1107–1116.
14. Cover, T., & Hart, P. (1967). Nearest Neighbor Pattern Classification. IEEE Transactions on Information Theory, 13 (1), 21–27. [↗](https://doi.org/10.1109/tit.1967.1053964)
15. Devlin, J., Chang, M.-W., Kenton, L., & Toutanova, K. (2018). [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://md.research.elamur.ai/free/en/papers/devlin-2019-bert-pre-training-deep-bidirectional/). CoRR, abs/1810.04805. [↗](https://arxiv.org/abs/1810.04805)
16. Fukushima, K. (1980). Neocognitron: A Self-Organizing Neural Network Model for a Mechanism of Pattern Recognition Unaffected by Shift in Position. Biological Cybernetics, 36 (4), 193–202. [↗](https://doi.org/10.1007/bf00344251)
17. Gambäck, B., Olsson, F., & Täckström, O. (2011). Active Learning for Dialogue Act Classification. In INTERSPEECH, pp. 1329–1332. [find](https://scholar.google.com/scholar?q=Gamba%CC%88ck%2C+B%2E%2C+Olsson%2C+F%2E%2C+%26+Ta%CC%88ckstro%CC%88m%2C+O%2E+%282011%29%2E+Active+Learning+for+Dialogue+Act+Classification%2E+In+INTERSPEECH%2C+pp%2E+1329%E2%80%931332%2E)
18. Gardner, M., Grus, J., Neumann, M., Tafjord, O., Dasigi, P., Liu, N. F., Peters, M., Schmitz, M., & Zettlemoyer, L. S. (2017). [AllenNLP: A Deep Semantic Natural Language Processing Platform](https://md.research.elamur.ai/free/en/papers/gardner-2018-allennlp-deep-semantic-natural-language/). CoRR, abs/1803.07640. [↗](https://arxiv.org/abs/1803.07640)
19. Godfrey, J. J., Holliman, E. C., & McDaniel, J. (1992). SWITCHBOARD: Telephone Speech Corpus for Research and Development. In ICASSP, Vol. 1, pp. 517–520. [find](https://scholar.google.com/scholar?q=Godfrey%2C+J%2E+J%2E%2C+Holliman%2C+E%2E+C%2E%2C+%26+McDaniel%2C+J%2E+%281992%29%2E+SWITCHBOARD%3A+Telephone+Speech+Corpus+for+Research+and+Development%2E+In+ICASSP%2C+Vol%2E+1%2C+pp%2E+517%E2%80%93520%2E)
20. Goldberg, Y. (2016). A Primer on Neural Network Models for Natural Language Processing. Journal of Artificial Intelligence Research, 57, 345–420. [↗](https://doi.org/10.1613/jair.4992)
21. Han, L., Kashyap, A. L., Finin, T., Mayfield, J., & Weese, J. (2013). [UMBC EBIQUITY-CORE: Semantic Textual Similarity Systems](https://md.research.elamur.ai/free/en/papers/han-2013-umbc-ebiquity-core-semantic-textual/). In *SEM, Vol. 1, pp. 44–52. [↗](https://doi.org/10.13016/m28w38626)
22. Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9 (8), 1735–1780. [↗](https://doi.org/10.1162/neco.1997.9.8.1735)
23. Honnibal, M., & Montani, I. (2017). spaCy 2: Natural Language Understanding with Bloom Embeddings, Convolutional Neural Networks and Incremental Parsing. [https://spacy.io/](https://spacy.io/). [↗](https://spacy.io/)
24. Janin, A., Baron, D., Edwards, J., Ellis, D., Gelbart, D., Morgan, N., Peskin, B., Pfau, T., Shriberg, E., Stolcke, A., & Wooters, C. (2003). The ICSI Meeting Corpus. In ICASSP, Vol. 1, pp. 364–367. [find](https://scholar.google.com/scholar?q=Janin%2C+A%2E%2C+Baron%2C+D%2E%2C+Edwards%2C+J%2E%2C+Ellis%2C+D%2E%2C+Gelbart%2C+D%2E%2C+Morgan%2C+N%2E%2C+Peskin%2C+B%2E%2C+Pfau%2C+T%2E%2C+Shriberg%2C+E%2E%2C+Stolcke%2C+A%2E%2C+%26+Wooters%2C+C%2E+%282003%29%2E+The+ICSI+Meeting+Corpus%2E+In+ICASSP%2C+Vol%2E+1%2C+pp%2E+364%E2%80%93367%2E)
25. Ji, Y., Haffari, G., & Eisenstein, J. (2016). A Latent Variable Recurrent Neural Network for Discourse Relation Language Models. In NAACL-HLT, pp. 332–342. [↗](https://doi.org/10.48550/arxiv.1603.01913)
26. Jurafsky, D., Shriberg, E., & Biasca, D. (1997). Switchboard SWBD-DAMSL Shallow-Discourse-Function Annotation Coders Manual. Tech. rep. Draft 13, University of Colorado, Institute of Cognitive Science. [find](https://scholar.google.com/scholar?q=Jurafsky%2C+D%2E%2C+Shriberg%2C+E%2E%2C+%26+Biasca%2C+D%2E+%281997%29%2E+Switchboard+SWBD%2DDAMSL+Shallow%2DDiscourse%2DFunction+Annotation+Coders+Manual%2E+Tech%2E+rep%2E+Draft+13%2C+University+of+Colorado%2C+Institute+of+Cognitive+Science)
27. Kalchbrenner, N., & Blunsom, P. (2013). [Recurrent Convolutional Neural Networks for Discourse Compositionality](https://md.research.elamur.ai/free/en/papers/kalchbrenner-2013-recurrent-convolutional-neural-networks-discourse/). In Workshop on Continuous Vector Space Models and their Compositionality, pp. 119–126.
28. Khanpour, H., Guntakandla, N., & Nielsen, R. (2016). [Dialogue Act Classification in Domain-Independent Conversations Using a Deep Recurrent Neural Network](https://md.research.elamur.ai/free/en/papers/khanpour-2016-dialogue-act-classification-domain-independent/). In COLING, pp. 2012–2021.
29. Kim, S., D’Haro, L. F., Banchs, R. E., Williams, J., & Henderson, M. (2016). The Fourth Dialog State Tracking Challenge. In International Workshop on Spoken Dialog Systems. [↗](https://doi.org/10.1007/978-981-10-2585-3_36)
30. Kim, Y. (2014). [Convolutional Neural Networks for Sentence Classification](https://md.research.elamur.ai/free/en/papers/kim-2014-convolutional-neural-networks-sentence-classification/). In EMNLP, pp. 1746–1751.
31. Kingma, D. P., & Ba, J. (2015). Adam: A Method for Stochastic Optimization. In ICLR. [↗](https://doi.org/10.48550/arxiv.1412.6980)
32. Král, P., & Cerisara, C. (2010). Dialogue Act Recognition Approaches. Computing and Informatics, 29 (2), 227–250. [find](https://scholar.google.com/scholar?q=Kra%CC%81l%2C+P%2E%2C+%26+Cerisara%2C+C%2E+%282010%29%2E+Dialogue+Act+Recognition+Approaches%2E+Computing+and+Informatics%2C+29+%282%29%2C+227%E2%80%93250%2E)
33. Lai, S., Xu, L., Liu, K., & Zhao, J. (2015). Recurrent Convolutional Neural Networks for Text Classification. In AAAI, pp. 2267–2273. [find](https://scholar.google.com/scholar?q=Lai%2C+S%2E%2C+Xu%2C+L%2E%2C+Liu%2C+K%2E%2C+%26+Zhao%2C+J%2E+%282015%29%2E+Recurrent+Convolutional+Neural+Networks+for+Text+Classification%2E+In+AAAI%2C+pp%2E+2267%E2%80%932273%2E)
34. Lavelli, A., Sebastiani, F., & Zanoli, R. (2004). Distributional Term Representations: An Experimental Comparison. In CIKM, pp. 615–624. [find](https://scholar.google.com/scholar?q=Lavelli%2C+A%2E%2C+Sebastiani%2C+F%2E%2C+%26+Zanoli%2C+R%2E+%282004%29%2E+Distributional+Term+Representations%3A+An+Experimental+Comparison%2E+In+CIKM%2C+pp%2E+615%E2%80%93624%2E)
35. Lee, J. Y., & Dernoncourt, F. (2016). [Sequential Short-Text Classification with Recurrent and Convolutional Neural Networks](https://md.research.elamur.ai/free/en/papers/lee-2016-sequential-short-text-classification-recurrent/). In NAACL-HLT, pp. 515–520.
36. Levy, O., & Goldberg, Y. (2014). Dependency-Based Word Embeddings. In ACL, Vol. 2, pp. 302–308. [find](https://scholar.google.com/scholar?q=Levy%2C+O%2E%2C+%26+Goldberg%2C+Y%2E+%282014%29%2E+Dependency%2DBased+Word+Embeddings%2E+In+ACL%2C+Vol%2E+2%2C+pp%2E+302%E2%80%93308%2E)
37. Liu, Y., Han, K., Tan, Z., & Lei, Y. (2017). [Using Context Information for Dialog Act Classification in DNN Framework](https://md.research.elamur.ai/free/en/papers/liu-2017-context-information-dialog-act-classification/). In EMNLP, pp. 2160–2168.
38. Manning, C. D. (2015). Computational Linguistics and Deep Learning. Computational Linguistics, 41 (4), 701–707. [↗](https://doi.org/10.1162/coli_a_00239)
39. Marcus, M. P., Santorini, B., & Marcinkiewicz, M. A. (1993). [Building a Large Annotated Corpus of English: The Penn Treebank](https://md.research.elamur.ai/free/en/papers/marcus-1993-building-large-annotated-corpus-english/). Computational Linguistics, 19 (2), 313–330. [↗](https://doi.org/10.21236/ada273556)
40. Mikolov, T., Grave, E., Bojanowski, P., Puhrsch, C., & Joulin, A. (2018). [Advances in Pre-Training Distributed Word Representations](https://md.research.elamur.ai/free/en/papers/mikolov-2017-advances-pre-training-distributed-word/). In LREC.
41. Mikolov, T., Karafiát, M., Burget, L., Cernocký, J., & Khudanpur, S. (2010). Recurrent Neural Network Based Language Model. In INTERSPEECH, pp. 1045–1048. [find](https://scholar.google.com/scholar?q=Mikolov%2C+T%2E%2C+Karafia%CC%81t%2C+M%2E%2C+Burget%2C+L%2E%2C+Cernocky%CC%81%2C+J%2E%2C+%26+Khudanpur%2C+S%2E+%282010%29%2E+Recurrent+Neural+Network+Based+Language+Model%2E+In+INTERSPEECH%2C+pp%2E+1045%E2%80%931048%2E)
42. Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., & Dean, J. (2013). [Distributed Representations of Words and Phrases and their Compositionality](https://md.research.elamur.ai/free/en/papers/mikolov-2013-distributed-representations-words-phrases-their/). In NIPS, pp. 3111–3119. [↗](https://doi.org/10.48550/arxiv.1310.4546)
43. Pennington, J., Socher, R., & Manning, C. D. (2014). [GloVe: Global Vectors for Word Representation](https://md.research.elamur.ai/free/en/papers/pennington-2014-glove-global-vectors-word-representation/). In EMNLP, pp. 1532–1543.
44. Peters, M. E., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K., & Zettlemoyer, L. (2018). [Deep Contextualized Word Representations](https://md.research.elamur.ai/free/en/papers/peters-2018-deep-contextualized-word-representations/). In NAACL-HLT, Vol. 1, pp. 2227–2237.
45. Petrov, S., Das, D., & McDonald, R. (2012). [A Universal Part-of-Speech Tagset](https://md.research.elamur.ai/free/en/papers/petrov-2012-universal-part-speech-tagset/). In LREC.
46. Radford, A., Narasimhan, K., Salimans, T., & Sutskever, I. (2018). Improving Language Understanding by Generative Pre-Training. Preprint. [find](https://scholar.google.com/scholar?q=Radford%2C+A%2E%2C+Narasimhan%2C+K%2E%2C+Salimans%2C+T%2E%2C+%26+Sutskever%2C+I%2E+%282018%29%2E+Improving+Language+Understanding+by+Generative+Pre%2DTraining%2E+Preprint%2E)
47. Ribeiro, E., Ribeiro, R., & de Matos, D. M. (2015). [The Influence of Context on Dialogue Act Recognition](https://md.research.elamur.ai/free/en/papers/ribeiro-2015-influence-context-dialogue-act-recognition/). CoRR, abs/1506.00839. [↗](https://arxiv.org/abs/1506.00839)
48. Ribeiro, E., Ribeiro, R., & de Matos, D. M. (2018). [A Study on Dialog Act Recognition using Character-Level Tokenization](https://md.research.elamur.ai/free/en/papers/ribeiro-2018-study-dialog-act-recognition-using/). In AIMSA, pp. 93–103. [↗](https://doi.org/10.1007/978-3-319-99344-7_9)
49. Ribeiro, E., Ribeiro, R., & de Matos, D. M. (2019). A Multilingual and Multidomain Study on Dialog Act Recognition using Character-Level Tokenization. Information, 10 (3), 94. [↗](https://doi.org/10.3390/info10030094)
50. Rotaru, M. (2002). Dialog Act Tagging Using Memory-Based Learning. Tech. rep., University of Pittsburgh. [find](https://scholar.google.com/scholar?q=Rotaru%2C+M%2E+%282002%29%2E+Dialog+Act+Tagging+Using+Memory%2DBased+Learning%2E+Tech%2E+rep%2E%2C+University+of+Pittsburgh%2E)
51. Schmidhuber, J. (1990). Dynamische Neuronale Netze und das Fundamentale Raumzeitliche Lernproblem. Ph.D. thesis, TU München. [find](https://scholar.google.com/scholar?q=Schmidhuber%2C+J%2E+%281990%29%2E+Dynamische+Neuronale+Netze+und+das+Fundamentale+Raumzeitliche+Lernproblem%2E+Ph%2ED%2E+thesis%2C+TU+Mu%CC%88nchen%2E)
52. Schmitt, A., Ultes, S., & Minker, W. (2012). A Parameterized and Annotated Spoken Dialog Corpus of the CMU Let’s Go Bus Information System. In LREC. [find](https://scholar.google.com/scholar?q=Schmitt%2C+A%2E%2C+Ultes%2C+S%2E%2C+%26+Minker%2C+W%2E+%282012%29%2E+A+Parameterized+and+Annotated+Spoken+Dialog+Corpus+of+the+CMU+Let%E2%80%99s+Go+Bus+Information+System%2E+In+LREC%2E)
53. Searle, J. R. (1969). [Speech Acts](https://md.research.elamur.ai/free/en/papers/searle-1969-speech-acts/): An Essay in the Philosophy of Language. Cambridge University Press. [↗](https://doi.org/10.1017/cbo9781139173438)
54. Shriberg, E., Dhillon, R., Bhagat, S., Ang, J., & Carvey, H. (2004). [The ICSI Meeting Recorder Dialog Act (MRDA) Corpus](https://md.research.elamur.ai/free/en/papers/shriberg-2004-icsi-meeting-recorder-dialog-act/). In SIGDIAL, pp. 97—-100. [↗](https://doi.org/10.21236/ada460980)
55. Sridhar, V. K. R., Bangalore, S., & Narayanan, S. (2009). Combining Lexical, Syntactic and Prosodic Cues for Improved Online Dialog Act Tagging. Computer Speech and Language, 23 (4), 407–422. [↗](https://doi.org/10.1016/j.csl.2008.12.001)
56. Stolcke, A., Coccaro, N., Bates, R., Taylor, P., Van Ess-Dykema, C., Ries, K., Shriberg, E., Jurafsky, D., Martin, R., & Meteer, M. (2000). [Dialogue Act Modeling for Automatic Tagging and Recognition of Conversational Speech](https://md.research.elamur.ai/free/en/papers/stolcke-2000-dialogue-act-modeling-automatic-tagging/). Computational Linguistics, 26 (3), 339–373. [↗](https://doi.org/10.1162/089120100561737)
57. Tiedemann, J. (2012). Parallel Data, Tools and Interfaces in OPUS. In LREC, pp. 2214– 2218. [find](https://scholar.google.com/scholar?q=Tiedemann%2C+J%2E+%282012%29%2E+Parallel+Data%2C+Tools+and+Interfaces+in+OPUS%2E+In+LREC%2C+pp%2E+2214%E2%80%93+2218%2E)
58. Tran, Q. H., Zukerman, I., & Haffari, G. (2017a). [A Generative Attentional Neural Network Model for Dialogue Act Classification](https://md.research.elamur.ai/free/en/papers/tran-2017-generative-attentional-neural-network-model/). In ACL, Vol. 2, pp. 524–529.
59. Tran, Q. H., Zukerman, I., & Haffari, G. (2017b). [A Hierarchical Neural Model for Learning Sequences of Dialogue Acts](https://md.research.elamur.ai/free/en/papers/tran-2017-hierarchical-neural-model-learning-sequences/). In EACL, Vol. 1, pp. 428–437.
60. Tran, Q. H., Zukerman, I., & Haffari, G. (2017c). [Preserving Distributional Information in Dialogue Act Classification](https://md.research.elamur.ai/free/en/papers/tran-2017-preserving-distributional-information-dialogue-act/). In EMNLP, pp. 2151–2156.
61. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017). [Attention is All You Need](https://md.research.elamur.ai/free/en/papers/vaswani-2017-attention-all-need/). In NIPS, pp. 5998–6008.
62. Webb, N., & Ferguson, M. (2010). [Automatic Extraction of Cue Phrases for Cross-corpus Dialogue Act Classification](https://md.research.elamur.ai/free/en/papers/webb-2010-automatic-extraction-cue-phrases-cross/). In COLING, pp. 1310–1317.
63. Zhang, X., Zhao, J., & LeCun, Y. (2015). [Character-Level Convolutional Networks for Text Classification](https://md.research.elamur.ai/free/en/papers/zhang-2015-character-level-convolutional-networks-text/). In NIPS, Vol. 1, pp. 649–657. [↗](https://doi.org/10.48550/arxiv.1509.01626)

*Updated 24 October 2026*