Detecting Age-Related Linguistic Patterns in Dialogue: Toward Adaptive Conversational Systems

Lennert Jansen; Arabella Sinclair; Margot J van der Goot; Raquel Fernández; Sandro Pezzelle

doi:10.4000/books.aaccademia.10678

Detecting Age-Related Linguistic Patterns in Dialogue: Toward Adaptive Conversational Systems

Lennert Jansen, Arabella Sinclair, Margot J van der Goot, Raquel Fernández, Sandro Pezzelle

University of Amsterdam

Research output: Chapter in Book/Report/Conference proceeding › Published conference contribution

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Abstract

This work explores an important dimension of variation in the language used by dialogue participants: their age. While previous work showed differences at various linguistic levels between age groups when experimenting with written discourse data (e.g., blog posts), previous work on dialogue has largely been limited to acoustic information related to voice and prosody. Detecting fine-grained linguistic properties of human dialogues is of crucial importance for developing AI-based conversational systems which are able to adapt to their human interlocutors. We therefore investigate whether, and to what extent, current text-based NLP models can detect such linguistic differences, and what the features driving their predictions are. We show that models achieve a fairly good performance on age-group prediction, though the task appears to be more challenging compared to discourse. Through in-depth analysis of the best models’ errors and the most predictive cues, we show that, in dialogue, differences among age groups mostly concern stylistic and lexical choices. We believe these findings can inform future work on developing controlled generation models for adaptive conversational systems.

Original language	English
Title of host publication	Proceedings of the Eighth Italian Conference on Computational Linguistics CliC-it 2021
Editors	Elisabetta Fersini, Marco Passarotti, Viviana Patti
Place of Publication	Torino
Publisher	Accademia University Press
Pages	184-190
Number of pages	8
ISBN (Electronic)	9791280136947
DOIs	https://doi.org/10.4000/books.aaccademia.10678
Publication status	Published - 20 Oct 2022
Event	CLiC-it 2021 Italian Conference on Computational Linguistics 2021 - Milan, Italy Duration: 29 Jun 2021 → 1 Jul 2021 https://clic2021.disco.unimib.it/

Publication series

Name	Collana dell'Associazione Italiana di Linguistica Computazionale
Publisher	Accademia University Press
ISSN (Electronic)	2531-4548

Conference

Conference	CLiC-it 2021 Italian Conference on Computational Linguistics 2021
Abbreviated title	CLiC-it 2021
Country/Territory	Italy
City	Milan
Period	29/06/21 → 1/07/21
Internet address	https://clic2021.disco.unimib.it/

Bibliographical note

This work received funding from the University of Amsterdam’s Research Priority Area Human(e) AI and from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 819455).

Code and data available at: https://github.com/lennertjansen/detecting-age-in-dialogue

Access to Document

10.4000/books.aaccademia.10678Licence: CC BY

Jansen_etal_Detecting_Age-Related_Linguistic_VOR
Copyright © 2021 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). https://creativecommons.org/licenses/by/4.0/
Final published version, 443 KBLicence: CC BY

Data and code from: Detecting Age-Related Linguistic Patterns in Dialogue: Toward Adaptive Conversational Systems
Sinclair, A. (Creator), University of Aberdeen, Jun 2022
DOI: 10.20392/efb3885c-9833-4b4d-bbfb-af2d6e9c11ed, https://github.com/lennertjansen/detecting-age-in-dialogue
Dataset

Cite this

Jansen, L., Sinclair, A., van der Goot, M. J., Fernández, R., & Pezzelle, S. (2022). Detecting Age-Related Linguistic Patterns in Dialogue: Toward Adaptive Conversational Systems. In E. Fersini, M. Passarotti, & V. Patti (Eds.), Proceedings of the Eighth Italian Conference on Computational Linguistics CliC-it 2021 (pp. 184-190). (Collana dell'Associazione Italiana di Linguistica Computazionale). Accademia University Press. https://doi.org/10.4000/books.aaccademia.10678

Detecting Age-Related Linguistic Patterns in Dialogue: Toward Adaptive Conversational Systems. / Jansen, Lennert; Sinclair, Arabella; van der Goot, Margot J et al.
Proceedings of the Eighth Italian Conference on Computational Linguistics CliC-it 2021. ed. / Elisabetta Fersini; Marco Passarotti; Viviana Patti. Torino: Accademia University Press, 2022. p. 184-190 (Collana dell'Associazione Italiana di Linguistica Computazionale).

Research output: Chapter in Book/Report/Conference proceeding › Published conference contribution

Jansen, L, Sinclair, A, van der Goot, MJ, Fernández, R & Pezzelle, S 2022, Detecting Age-Related Linguistic Patterns in Dialogue: Toward Adaptive Conversational Systems. in E Fersini, M Passarotti & V Patti (eds), Proceedings of the Eighth Italian Conference on Computational Linguistics CliC-it 2021. Collana dell'Associazione Italiana di Linguistica Computazionale, Accademia University Press, Torino, pp. 184-190, CLiC-it 2021 Italian Conference on Computational Linguistics 2021, Milan, Italy, 29/06/21. https://doi.org/10.4000/books.aaccademia.10678

Jansen L, Sinclair A, van der Goot MJ, Fernández R, Pezzelle S. Detecting Age-Related Linguistic Patterns in Dialogue: Toward Adaptive Conversational Systems. In Fersini E, Passarotti M, Patti V, editors, Proceedings of the Eighth Italian Conference on Computational Linguistics CliC-it 2021. Torino: Accademia University Press. 2022. p. 184-190. (Collana dell'Associazione Italiana di Linguistica Computazionale). Epub 2022 Oct 20. doi: 10.4000/books.aaccademia.10678

Jansen, Lennert ; Sinclair, Arabella ; van der Goot, Margot J et al. / Detecting Age-Related Linguistic Patterns in Dialogue : Toward Adaptive Conversational Systems. Proceedings of the Eighth Italian Conference on Computational Linguistics CliC-it 2021. editor / Elisabetta Fersini ; Marco Passarotti ; Viviana Patti. Torino : Accademia University Press, 2022. pp. 184-190 (Collana dell'Associazione Italiana di Linguistica Computazionale).

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abstract = "This work explores an important dimension of variation in the language used by dialogue participants: their age. While previous work showed differences at various linguistic levels between age groups when experimenting with written discourse data (e.g., blog posts), previous work on dialogue has largely been limited to acoustic information related to voice and prosody. Detecting fine-grained linguistic properties of human dialogues is of crucial importance for developing AI-based conversational systems which are able to adapt to their human interlocutors. We therefore investigate whether, and to what extent, current text-based NLP models can detect such linguistic differences, and what the features driving their predictions are. We show that models achieve a fairly good performance on age-group prediction, though the task appears to be more challenging compared to discourse. Through in-depth analysis of the best models{\textquoteright} errors and the most predictive cues, we show that, in dialogue, differences among age groups mostly concern stylistic and lexical choices. We believe these findings can inform future work on developing controlled generation models for adaptive conversational systems.",

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N1 - This work received funding from the University of Amsterdam’s Research Priority Area Human(e) AI and from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 819455). Code and data available at: https://github.com/lennertjansen/detecting-age-in-dialogue

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N2 - This work explores an important dimension of variation in the language used by dialogue participants: their age. While previous work showed differences at various linguistic levels between age groups when experimenting with written discourse data (e.g., blog posts), previous work on dialogue has largely been limited to acoustic information related to voice and prosody. Detecting fine-grained linguistic properties of human dialogues is of crucial importance for developing AI-based conversational systems which are able to adapt to their human interlocutors. We therefore investigate whether, and to what extent, current text-based NLP models can detect such linguistic differences, and what the features driving their predictions are. We show that models achieve a fairly good performance on age-group prediction, though the task appears to be more challenging compared to discourse. Through in-depth analysis of the best models’ errors and the most predictive cues, we show that, in dialogue, differences among age groups mostly concern stylistic and lexical choices. We believe these findings can inform future work on developing controlled generation models for adaptive conversational systems.

AB - This work explores an important dimension of variation in the language used by dialogue participants: their age. While previous work showed differences at various linguistic levels between age groups when experimenting with written discourse data (e.g., blog posts), previous work on dialogue has largely been limited to acoustic information related to voice and prosody. Detecting fine-grained linguistic properties of human dialogues is of crucial importance for developing AI-based conversational systems which are able to adapt to their human interlocutors. We therefore investigate whether, and to what extent, current text-based NLP models can detect such linguistic differences, and what the features driving their predictions are. We show that models achieve a fairly good performance on age-group prediction, though the task appears to be more challenging compared to discourse. Through in-depth analysis of the best models’ errors and the most predictive cues, we show that, in dialogue, differences among age groups mostly concern stylistic and lexical choices. We believe these findings can inform future work on developing controlled generation models for adaptive conversational systems.

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Detecting Age-Related Linguistic Patterns in Dialogue: Toward Adaptive Conversational Systems

Abstract

Publication series

Conference

Bibliographical note

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Datasets

Data and code from: Detecting Age-Related Linguistic Patterns in Dialogue: Toward Adaptive Conversational Systems

Cite this