@inproceedings{bhattacharya-etal-2026-cross,
title = "A Cross-lingual Comparison of Human and Classification Model Entrainment Behavior in Code-switched Speech Settings",
author = "Bhattacharya, Debasmita and
Ding, Siying and
Nguyen, Alayna and
Hirschberg, Julia",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.798/",
pages = "16906--16936",
ISBN = "979-8-89176-462-0",
abstract = "Conversational entrainment is well-studied in monolingual and written contexts, but remains underexplored in spoken code-switching (CSW). We present a cross-lingual analysis of entrainment in Mandarin-English, Hindi-English, and Spanish-English dialogue, replicating and extending an established Spanish-English framework (Bhattacharya et al., 2024) to two new language pairs, and show that, while lexical entrainment generalizes across language pairs, entrainment over acoustic-prosodic and CSW style aspects exhibits context-specific variation. We build on these findings by asking whether classification models capture these human behavioral patterns. Using feature importance and ablation analyses, we find that classical and Transformer-based classifiers detect entrainment reasonably well but consistently prioritize features other than those most salient to human entraining behavior. Our approach introduces a human-grounded framework for evaluating model decision-making in multilingual stylistic contexts, and suggests challenges for developing conversational agents capable of producing naturalistic code-switched speech."
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<abstract>Conversational entrainment is well-studied in monolingual and written contexts, but remains underexplored in spoken code-switching (CSW). We present a cross-lingual analysis of entrainment in Mandarin-English, Hindi-English, and Spanish-English dialogue, replicating and extending an established Spanish-English framework (Bhattacharya et al., 2024) to two new language pairs, and show that, while lexical entrainment generalizes across language pairs, entrainment over acoustic-prosodic and CSW style aspects exhibits context-specific variation. We build on these findings by asking whether classification models capture these human behavioral patterns. Using feature importance and ablation analyses, we find that classical and Transformer-based classifiers detect entrainment reasonably well but consistently prioritize features other than those most salient to human entraining behavior. Our approach introduces a human-grounded framework for evaluating model decision-making in multilingual stylistic contexts, and suggests challenges for developing conversational agents capable of producing naturalistic code-switched speech.</abstract>
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%0 Conference Proceedings
%T A Cross-lingual Comparison of Human and Classification Model Entrainment Behavior in Code-switched Speech Settings
%A Bhattacharya, Debasmita
%A Ding, Siying
%A Nguyen, Alayna
%A Hirschberg, Julia
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F bhattacharya-etal-2026-cross
%X Conversational entrainment is well-studied in monolingual and written contexts, but remains underexplored in spoken code-switching (CSW). We present a cross-lingual analysis of entrainment in Mandarin-English, Hindi-English, and Spanish-English dialogue, replicating and extending an established Spanish-English framework (Bhattacharya et al., 2024) to two new language pairs, and show that, while lexical entrainment generalizes across language pairs, entrainment over acoustic-prosodic and CSW style aspects exhibits context-specific variation. We build on these findings by asking whether classification models capture these human behavioral patterns. Using feature importance and ablation analyses, we find that classical and Transformer-based classifiers detect entrainment reasonably well but consistently prioritize features other than those most salient to human entraining behavior. Our approach introduces a human-grounded framework for evaluating model decision-making in multilingual stylistic contexts, and suggests challenges for developing conversational agents capable of producing naturalistic code-switched speech.
%U https://aclanthology.org/2026.findings-emnlp.798/
%P 16906-16936
Markdown (Informal)
[A Cross-lingual Comparison of Human and Classification Model Entrainment Behavior in Code-switched Speech Settings](https://aclanthology.org/2026.findings-emnlp.798/) (Bhattacharya et al., Findings 2026)
ACL