The Gricean Maxims in NLP — A Survey

What it is about

A survey of 78 papers (1990–2024) that use Grice's maxims of Quantity, Quality, Relation and Manner in NLP. It covers datasets and benchmarks (GRICE, DiPlomat, PUB) and generation tasks: referring expressions (Reiter 1990; Dale & Reiter's Incremental Algorithm; minimality metrics in the TUNA/REG shared tasks), data-to-text, summarisation, translation and image description. On the understanding side it covers inverting the maxims to learn implicit rules from terse texts (Sorower et al. 2011), sentiment tests built by flouting maxims, QA answer ranking, question generation that uses only common-ground information, and conversational agents. It also covers LLM evaluation. The closing section names open problems: the maxims are culture-dependent, they conflict with each other, and they are mostly used as loose evaluation criteria rather than as computable constraints.

Ideas

• I1. Implement Dale & Reiter's Incremental Algorithm as the referring-expression chooser wherever Mova generates text about an entity (lesson summaries, the family's replies in world/src/talk.rs). Walk a fixed preference order of attributes (type, colour, size, …), add an attribute only if it rules out a distractor, and stop when the referent is unique; use a pronoun when the referent is the most salient one in the frame resolver. It is deterministic, and the attributes chosen are the explanation. Chapter 35 §11 already names generating referring expressions; the RSA reference game in prag/src/rsa.rs can act as a check on the same small scenes.
• I2. «Inverting Grice» (Sorower et al. 2011): a text says just enough for a reader to infer the rest, so a fact that is regularly left unsaid after another is a candidate rule. This fits the rules the reader learns itself (prag::rules, world frames rules): count frames that co-occur across lessons, and treat a frame that is never stated when its usual antecedent is stated as an implied default. Check each candidate rule against held-out lessons.
• I3. The gap-focused question criterion is that a question must use only common-ground information and its answer must not be in it yet. This is the same givenness test as QUDeval, already in world/src/qud.rs. Use it as a gate on the school questions the teacher writes for a lesson: reject a question whose content words are not in the lesson text so far (answer leakage or hallucination).

For Mova

33. Implicature and presupposition §2 (the maxims), 35. Reference and coreference §11 (generating referring expressions); world/src/talk.rs, prag/src/rsa.rs, prag/src/rules.rs, world/src/qud.rs; measures: uniqueness and minimality of generated descriptions on small test scenes; precision of induced rules on held-out lessons.

Topics: Implicature

Cite

The paper itself, not this card. BibTeX from the library's record of the work. DOI 10.18653/v1/2024.inlg-main.39

@inproceedings{krause2024gricean,
  title = {{The Gricean Maxims in NLP — A Survey}},
  author = {Krause, Lea and Vossen, Piek T.J.M.},
  booktitle = {Proceedings of the 17th International Natural Language Generation Conference},
  year = {2024},
  doi = {10.18653/v1/2024.inlg-main.39},
  url = {https://doi.org/10.18653/v1/2024.inlg-main.39}
}

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