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Determining Verb Phrase Referents in Dialogs

Paper · 1981 · Ann E. Robinson

Summary

Robinson tackles how to work out which real-world action a verb phrase points to in a task-oriented dialog. Work on noun phrase reference was well established, but verb phrase reference had received little attention. The problem is sharpest with generic verbs like "do", "have" or "use" ("I've done it"). It also arises with specific verbs whose meaning depends on their objects, such as different gluing or cutting steps. A further question is whether a verb names a general activity or one concrete act. The proposed approach combines three sources of knowledge: language, the task domain and the dialog itself. Actions are represented as hierarchical process models with participants, preconditions, effects and ordered substeps. These are tied to a goal structure and to Grosz/Sidner-style focusing. Algorithms resolve verb references against this structure and infer the resulting world state, such as which steps and substeps are finished. The methods were built into TDUS, an expert system that coaches an apprentice through assembling an air compressor. They are shown working on a sample dialog. The work matters because it shows that interpreting utterances needs many knowledge types working together.

Key points

  • Implementation and domain. The algorithms run in TDUS (Robinson et al., 1980), which acts as an expert in an air compressor/pump assembly dialog.
  • Action representation. Actions are encoded as "process models" (Grosz et al., 1977), an extension of Hendrix's network formalism.
    • Each model records participants, preconditions, effects and a plot of partially ordered substeps.
    • Performed actions are instantiated and placed on a time lattice to track assembly progress.
  • Goal types. Goals are either domain goals or knowledge-state goals (e.g., knowing where the bolts are). Each kind can be direct or potential. TDUS does not yet support potential knowledge-state goals.
  • Sample dialog behaviour.
    • TDUS detects a wrong proposed next step and lists the valid next steps.
    • It resolves "do" as "install" by referring back to the prior turn.
    • From "I've done it", it infers that a step and all its substeps are complete.
  • Future work. Open problems include:
    • better integration of top-down and bottom-up search;
    • criteria for when to stop linking an action to a plan;
    • handling domains with weaker task models.

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

🔬SRI InternationalMenlo Park · United States23 works · 7,208 citations

 Fields, topics and concepts

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 works

🧭 Topics 1Research subjects and languages the work is about.Dialogue systems · 624 works

Cite

Cite as Ann E. Robinson. 1981. Determining Verb Phrase Referents in Dialogs. Computational Linguistics.

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

@article{robinson1981determining,
  title = {{Determining Verb Phrase Referents in Dialogs}},
  author = {Ann E. Robinson},
  journal = {Computational Linguistics},
  year = {1981},
  url = {https://aclanthology.org/J81-1001/}
}

References

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

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