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Computational Politeness in Natural Language Processing: A Survey

Paper · 2024 · Priyanshu Priya, Mauajama Firdaus and Asif Ekbal

Summary

This survey maps the research area that tries to detect and generate politeness in text automatically. It starts from linguistic theory and covers several traditions: maxim-based accounts (Grice's Cooperative Principle and Leech's Politeness Principle), face-based, discursive, relational and frame-based views. It then reviews computational work from the early SVM study of online requests by Danescu-Niculescu-Mizil et al. onward.

The authors see four milestones in the field:

  • supervised and weakly-supervised feature extraction,
  • adding context beyond the target utterance,
  • studying politeness across social factors,
  • linking politeness to other socio-linguistic cues such as emotion and dialog acts.

The survey catalogues problem definitions, datasets, approaches, reported performance values, trends and open issues. Its main reference tool is a comparative table of prior papers by feature type, annotation technique and dataset.

The central difficulty it identifies is context dependence. Polite markers come from a shared pool, but their weight and valence change with setting, speaker, goal and culture. This makes the survey a useful map for anyone building dialogue or pragmatics systems that must model social behaviour.

Key points

  • Politeness can be an act of commission ("please", "thank you") or of omission, such as choosing not to contradict someone. Marker meaning shifts with context (workplace vs. family), speaker, goal (buyer vs. seller) and culture.
  • Leech's Politeness Principle has six maxims: Tact, Generosity, Approbation, Modesty, Agreement and Sympathy. It complements the four Gricean maxims: the CP explains how indirect meaning is conveyed, and the PP explains why speakers choose indirectness.
  • Datasets are labelled either manually (gold standard) or automatically through supervision, and supervision is the dominant source of labels. Statistical models use lexical, syntactic and affect-based features. Deep learning and, more recently, LLM evaluation now appear for both identification and generation.
  • The main reported issues are the scarcity of politeness-annotated corpora and the uneven quality of manual annotation.
  • Proposed future directions include:
    • variation by age, gender and culture (e.g., polite response generation conditioned on user age and gender by Firdaus et al. and Mishra et al.),
    • links with emotion (the multi-task model of Priya et al.),
    • low-resource languages and new domains beyond customer care and fashion,
    • social-good uses such as counselling and persuasion.

Interesting ideas

  • I1. Politeness can lie in what a speaker avoids saying, not only in explicit markers, so a model must compare an utterance against the moves that were expected and skipped.
  • I2. Pairing the Cooperative Principle with the Politeness Principle splits indirectness into mechanism (which maxim is bent) and motive (which social cost is being reduced). An example is a waiter delivering shocking news disguised as a joke.
  • I3. The same marker can change weight and valence depending on the speakers' relationship and goals, so politeness features should be conditioned on roles rather than scored in isolation.
  • I4. The survey warns that manual politeness annotation is noisy while most labels come from supervision, which argues for auditing automatically produced labels against a small trusted set.
  • I5. Modelling politeness jointly with correlated cues such as emotion and dialog acts lets each signal inform the others instead of being predicted alone.

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 record expected but absent moves (e.g., no contradiction after a disputed claim) as evidence for a politeness or deference reading.
  • I2 (frames): when the implicature module detects a flouted Gricean maxim, it could check Tact/Modesty/Agreement as the explanation and attach that motive to the explainable trace.
  • I3 (world): store participant roles and relations (seller/buyer, parent/child) in the world of the text, so that later readings of requests and refusals can consult them.
  • I4 (learn): silver pragmatic labels agreed by witness parsers or LLM teachers could be spot-checked against a small hand-verified politeness subset used as a control.
  • I5 (frames): prag could cross-check its politeness reading with detected emotion and speech act, and flag inconsistent combinations as candidates for irony or sarcasm.

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

🎓University of AlbertaEdmonton · Canada51 works · 5,017 citations🎓Indian Institute of Technology PatnaPatna · India21 works · 374 citations

 Fields, topics and concepts

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 18,614 worksArtificial Intelligence · 17,965 worksArts and Humanities · 17,450 worksLanguage and Linguistics · 17,296 works

Cite

Cite as Priyanshu Priya, Mauajama Firdaus and Asif Ekbal. 2024. Computational Politeness in Natural Language Processing: A Survey. ACM Comput. Surv. 56, 9, Article 241 (May 2024).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.1145/3654660 · arXiv 2407.12814

@inproceedings{priya2024computational,
  title = {{Computational Politeness in Natural Language Processing: A Survey}},
  author = {Priyanshu Priya, Mauajama Firdaus and Asif Ekbal},
  booktitle = {ACM Comput. Surv. 56, 9, Article 241 (May 2024)},
  year = {2024},
  doi = {10.1145/3654660},
  eprint = {2407.12814},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.1145/3654660}
}

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