📄 Toward Givenness Hierarchy Theoretic Natural Language Generation

What it is about

A robot that talks with people must choose between it, this N, that N and the N. The Givenness Hierarchy (GH) says that the form reflects the cognitive status a referent has in the hearer's mind: in focus, activated, familiar, uniquely identifiable, referential, type identifiable. Earlier work used GH data structures to understand anaphora. This paper addresses generation. It proposes a per-entity Bayesian filter that keeps a distribution over three statuses (in focus, activated, familiar), updated by how each mention is used (not mentioned, mentioned in a non-topic role, mentioned as topic). It also gives a Describe algorithm that picks a form from the status, uses only same-or-higher-status entities as distractors, and adjusts the form for distance. No experiment is reported; the authors plan a human study of preferred referring forms.

Ideas

• I1. A count-based status tracker: per entity, a state among the three statuses, changed by a fixed transition table keyed on the grammatical role of the mention (subject or topic moves to in focus; object to activated; others to familiar). Store the table in a BTreeMap so updates are deterministic.
• I2. Form rule: choose it if the entity is in focus with no competing distractor, this N if activated, that N if familiar, and the N otherwise. Check the rule against the pronoun and demonstrative choices in Mova's own coreference data.
• I3. Narrow the distractor set by status: only entities of the same or higher status need ruling out. This shrinks the candidate set checked in coref/src/sieve.rs.
• I4. Negative control: a random status assignment must give worse form accuracy than the tracker.

For Mova

Chapter: languages/en/35-reference-and-coreference.md, and languages/en/12-pronouns-determiners-numerals.md for the forms. Code: coref/src/sieve.rs and coref/src/mention.rs. Measure: form accuracy (pronoun, demonstrative, definite) on a held-out set of Mova texts, and the mean number of distractors checked per mention; two runs must match.

Cite

The paper itself, not this card. BibTeX from the library's record of the work. arXiv 2007.16009

@misc{pal2020toward,
  title = {{Toward Givenness Hierarchy Theoretic Natural Language Generation}},
  author = {Pal, Poulomi and Williams, Tom},
  year = {2020},
  eprint = {2007.16009},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2007.16009}
}

References

2 references, 0 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Arnold, J. E. (2016). Explicit and emergent mechanisms of information status. Topics in Cog. Sci.. ↗
2. Callaway, C. B., & Lester, J. C. (2002). Pronominalization in generated discourse and dialogue. Proc. ACL (pp. 88–95). find

This page in Markdown → https://md.research.elamur.ai/free/en/papers/pal-2020-toward-givenness-hierarchy-theoretic-natural/