@inproceedings{alipanah-etal-2026-synthetic,
title = "A Synthetic Request-Reply Email Corpus Annotated with Document-Level Politeness and Sentence-Level Face Acts",
author = "Alipanah, Roshad and
Barriere, Valentin and
Baier, Jorge A.",
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.22/",
pages = "419--428",
ISBN = "979-8-89176-462-0",
abstract = "Politeness is key to building healthy communication. Brown and Levinson{'}s influential theory of politeness (BL{'}s theory) proposes that polite speech can be characterized by the presence of face-saving speech acts while impolite speech is characterized by the presence of face-threatening speech acts. Most existing politeness resources neither operationalize nor control politeness under BL{'}s theory; instead, they typically consist of short utterances with an intuitive account of politeness. In this paper, we introduce a long-text corpus that includes sentence-level face-act annotations and document-level politeness scores. Following BL{'}s theory, we annotate politeness along three dimensions: Directness vs. Indirectness, Positive Face Saving, and Negative Face Saving, and define Overall Politeness as their average. We formulate face-act tagging as a multi-label classification task and compare context-agnostic sentence classifiers to context-sensitive variants and sequence-labeling models. We also model document-level politeness via multi-target regression using the text, and enhance the regression model with the predictions of our face-act classifier. We show that context-sensitive classifiers outperform context-free classifiers for face-act tagging. Moreover, we demonstrate that for document-level politeness regression, enhancing the text with face-act predictions yields better performance. Our code and data are available online: alipanahroshad-oss/email-politeness-corpus"
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<abstract>Politeness is key to building healthy communication. Brown and Levinson’s influential theory of politeness (BL’s theory) proposes that polite speech can be characterized by the presence of face-saving speech acts while impolite speech is characterized by the presence of face-threatening speech acts. Most existing politeness resources neither operationalize nor control politeness under BL’s theory; instead, they typically consist of short utterances with an intuitive account of politeness. In this paper, we introduce a long-text corpus that includes sentence-level face-act annotations and document-level politeness scores. Following BL’s theory, we annotate politeness along three dimensions: Directness vs. Indirectness, Positive Face Saving, and Negative Face Saving, and define Overall Politeness as their average. We formulate face-act tagging as a multi-label classification task and compare context-agnostic sentence classifiers to context-sensitive variants and sequence-labeling models. We also model document-level politeness via multi-target regression using the text, and enhance the regression model with the predictions of our face-act classifier. We show that context-sensitive classifiers outperform context-free classifiers for face-act tagging. Moreover, we demonstrate that for document-level politeness regression, enhancing the text with face-act predictions yields better performance. Our code and data are available online: alipanahroshad-oss/email-politeness-corpus</abstract>
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%0 Conference Proceedings
%T A Synthetic Request-Reply Email Corpus Annotated with Document-Level Politeness and Sentence-Level Face Acts
%A Alipanah, Roshad
%A Barriere, Valentin
%A Baier, Jorge A.
%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 alipanah-etal-2026-synthetic
%X Politeness is key to building healthy communication. Brown and Levinson’s influential theory of politeness (BL’s theory) proposes that polite speech can be characterized by the presence of face-saving speech acts while impolite speech is characterized by the presence of face-threatening speech acts. Most existing politeness resources neither operationalize nor control politeness under BL’s theory; instead, they typically consist of short utterances with an intuitive account of politeness. In this paper, we introduce a long-text corpus that includes sentence-level face-act annotations and document-level politeness scores. Following BL’s theory, we annotate politeness along three dimensions: Directness vs. Indirectness, Positive Face Saving, and Negative Face Saving, and define Overall Politeness as their average. We formulate face-act tagging as a multi-label classification task and compare context-agnostic sentence classifiers to context-sensitive variants and sequence-labeling models. We also model document-level politeness via multi-target regression using the text, and enhance the regression model with the predictions of our face-act classifier. We show that context-sensitive classifiers outperform context-free classifiers for face-act tagging. Moreover, we demonstrate that for document-level politeness regression, enhancing the text with face-act predictions yields better performance. Our code and data are available online: alipanahroshad-oss/email-politeness-corpus
%U https://aclanthology.org/2026.findings-emnlp.22/
%P 419-428
Markdown (Informal)
[A Synthetic Request-Reply Email Corpus Annotated with Document-Level Politeness and Sentence-Level Face Acts](https://aclanthology.org/2026.findings-emnlp.22/) (Alipanah et al., Findings 2026)
ACL