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Responding Intelligently to Unparsable Inputs

Paper · 1980 · Ralph M. Weischedel and John E. Black

Summary

Practical natural language interfaces, such as database front ends, consultation programs and office tools, will inevitably receive inputs outside their coverage. The authors argue that such a system must tell users precisely why an input failed so they can reformulate it. They propose three techniques, each implemented in one of two systems: a tutor for German reading comprehension and a system that computes presuppositions and entailments.

  1. Presupposition checking. The system verifies the given information of an input against its world model and reports any assumption that does not hold.
  2. Relaxation of tests during parsing. Grammar constraints are loosened so that ill-formed sentences can still be translated and the violation can be flagged.
  3. Heuristics for failed parses. When no parse exists, the system uses the expectations of a top-down, left-to-right parser to describe where and why it got stuck.

The methods diagnose problems but do not propose rephrasings. The work matters because helpful failure feedback is presented as a precondition for non-programmers to accept natural language systems.

Key points

  • Motivating example. Woods's lunar rock question about "all your samples" broke the system in three ways:
    • Syntax: a predeterminer before a determiner was not covered.
    • Semantics: there were no rules or data for sample weight.
    • Pragmatics: the system was never modelled as owning the samples.
  • Psychological basis. Presupposition handling draws on Clark and Haviland's given/new model. The system checks given information in context before asserting new information, and the computation is organized in an augmented transition network (ATN) and lexicon.
  • Error diagnosis in the German tutor.
    • It catches misunderstood questions when the answer does not entail the question's given information.
    • It reports definite noun phrases that refer to nothing.
    • It catches misused words, such as essen (human eater) versus fressen (animal eater).
  • Anticipated mistakes. Common learner errors are encoded as explicit ATN paths that both translate the answer and generate a correction message. An example is a past participle not placed at the end of the clause ("Ich habe gegessen das Fleisch").
  • Related work. Kaplan (1977, 1979) extended presupposition-based cooperative responses to database queries that would return empty answers.

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 DelawareNewark · United States20 works · 1,056 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.Implicature · 138 works

Cite

Cite as Ralph M. Weischedel and John E. Black. 1980. Responding Intelligently to Unparsable Inputs. Computational Linguistics.

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

@article{weischedel1980responding,
  title = {{Responding Intelligently to Unparsable Inputs}},
  author = {Ralph M. Weischedel and John E. Black},
  journal = {Computational Linguistics},
  year = {1980},
  doi = {10.5555/972439.972442},
  url = {https://doi.org/10.5555/972439.972442}
}

References

27 references, 1 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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  4. Bobrow, Robert J., "The RUS System," in B. L. Webber and R. Bobrow, Research in Natural Language Understanding, BBN Report 3878, Bolt Beranek and Newman Inc., Cambridge, MA, 1978. find
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