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Computational Generation of Referring Expressions: A Survey

Summary

This survey covers computational work on referring expression generation (REG), the NLG subtask of choosing how to describe an entity so that a hearer can pick it out in context. The authors trace the field from early utterance-planning systems (SHRDLU, KAMP, BERTRAND) to the 1990s focus on distinguishing descriptions, anchored by Dale and Reiter (1995). They then review newer directions: extending algorithms to vague, relational, and plural descriptions; reformulating REG using formalisms like Graph Theory and Description Logic, which ties it to well-developed Knowledge Representation techniques; and building corpora and shared evaluations. A recurring finding is that shortest descriptions are expensive to compute and are not reliably what speakers produce or what helps listeners. The authors argue that evaluation should move beyond imitating human output toward usefulness for the hearer, and that future work should handle larger, more realistic domains and reference embedded in text and dialogue. The survey matters because it gathers results scattered across many venues into one assessment of the field.

Key points

  • Reiter and Dale (2000) split REG into three decisions: choosing the expression type (proper name, pronoun, or description), selecting content, and realizing it linguistically. Mellish et al. (2006) found that nearly every practical NLG system contains some kind of REG module.
  • Gricean reasoning (Maxim of Quantity) explains why overly detailed descriptions can mislead. For example, saying "the brown wooden table" when only one table exists can trigger unintended implicatures.
  • Appelt's KAMP already showed key later themes:
    • doubts that minimal descriptions are natural;
    • use of Rosch-style basic categories;
    • awareness of computational cost.
  • Shared evaluation challenges named in the survey include TUNA REG, GREC, and GIVE. The authors note these mostly involve small domains and simple expressions.
  • Most evaluations measure human-likeness against corpora. The authors recommend hearer-oriented measures, such as how quickly and accurately readers identify the referent, especially for logically complex descriptions.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Natural language processing

Cited by

105 papers of our library cite it (of the 140884 whose bibliographies we read); 8 of them have a page here, 97 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.1162/coli_a_00088

@article{krahmer2012computational,
  title = {{Computational Generation of Referring Expressions: A Survey}},
  author = {Krahmer, Emiel and Deemter, Kees van},
  journal = {Computational Linguistics},
  year = {2012},
  doi = {10.1162/coli\_a\_00088},
  url = {https://doi.org/10.1162/coli_a_00088}
}

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