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Generating Indirect Answers to Yes-No Questions

Paper · 1994 · Nancy Green and Sandra Carberry

Summary

Green and Carberry address how a dialogue system can answer a Yes-No question without saying "yes" or "no" outright, so that the answer is carried by implicature. Their implemented model, written in Common Lisp, encodes pragmatic knowledge as two parts: domain-independent discourse plan operators that describe a full answer, and coherence rules based on RST-style relations. A full answer pairs a direct answer (the nucleus) with optional related information (satellites). The same knowledge drives two directions: interpretation is treated as inferring a plan, and generation as building one. The authors found that this knowledge alone cannot decide which extra content to include. They therefore add stimulus conditions, which are Horn-clause triggers that create new speaker goals. Generation then runs in two phases. Content planning first assembles a full response. Plan pruning then removes the parts the hearer can infer, which in suitable contexts leaves only an indirect answer. The authors claim theirs is the first system to produce a wide range of indirect and full answers.

Key points

  • Corpus figures cited from Stenström: about 13% of replies to Yes-No questions in spoken English are indirect, and 85% of direct answers come with extra information.
  • The top-level operators are Answer-yes, Answer-no, Answer-hedged, Answer-maybe and Answer-maybe-not. Their satellites include Use-obstacle, Use-contrast, Use-cause, Use-condition, Use-elaboration, Use-otherwise, Use-result, Use-usually and Use-possible-cause.
  • Besides the RST subject-matter relations, the authors identified new coherence relations: cr-obstacle, cr-possible-cause, cr-possible-obstacle and cr-usually.
  • Data came from Hirschberg's and Stenström's studies, transcripts of two-person dialogues, and constructed examples. The system ran on a Sun SPARCstation.
  • A satellite is included only if every one of its applicability conditions is met and one or more of its stimulus conditions is also satisfied. Examples of stimulus conditions are explanation-indicated and clarify-extent-indicated.

Interesting ideas

  • I1. The model is reversible: one set of plan operators and coherence rules supports both understanding and producing answers, so the two directions cannot drift apart.
  • I2. Conditions that make content allowed (applicability) are kept separate from conditions that make the speaker want to say it (stimulus). This avoids both over-informing and under-informing without deep reasoning about goals.
  • I3. Generation first plans the full response and then prunes whatever the hearer can recover, so conciseness becomes an explicit inference check.
  • I4. A nucleus may be omitted without making the text incoherent if the rest of the reply implies it, which departs from standard RST.
  • I5. Answers are graded beyond yes and no into hedged, maybe and maybe-not.

For Mova

How these ideas could serve Mova, our small open language model — its files, stages and tests:

  • I1 (frames): prag could read "My car's not running" as an implied No by checking which coherence relation, such as an obstacle, plausibly links the reply to a candidate yes or no.
  • I2 (back): the generator could gate optional clauses with explicit, explainable trigger rules rather than adding every fact that is licensed.
  • I3 (back): the text → graph → text compressor could drop a proposition only after verifying that the remaining graph still entails it.
  • I4 (world): the world of a text could store implied answers as derived states, each linked to the satellite sentence and relation that justify it.
  • I5 (answer): fable and school-test readers could output these graded verdicts with their supporting relation, instead of forcing a binary choice on uncertain items.

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

Cite

Cite as Nancy Green and Sandra Carberry. 1994. Generating Indirect Answers to Yes-No Questions. Proceedings of the Seventh International Workshop on Natural Language Generation.

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

@inproceedings{green1994generating,
  title = {{Generating Indirect Answers to Yes-No Questions}},
  author = {Nancy Green and Sandra Carberry},
  booktitle = {Proceedings of the Seventh International Workshop on Natural Language Generation},
  year = {1994},
  doi = {10.3115/1641417.1641439},
  url = {https://doi.org/10.3115/1641417.1641439}
}

References

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