A Hybrid Reasoning Model for Indirect Answers
Summary
Green and Carberry address how a dialogue system can understand and produce replies to English Yes-No questions that never say "yes" or "no" outright but still let the asker work out the answer. Such replies are common, so robust dialogue systems need to handle them. The authors drew on earlier studies, the American Express transcripts (1992) and constructed examples. They encode pragmatic knowledge about answering as domain-independent discourse plan operators and as coherence rules modelled on RST relations. Interpretation is treated as plan recognition, with logical inference used only for consistency checks and hypothesis generation. Generation is treated as plan construction. First, stimulus conditions decide which extra information is worth adding. Second, the plan is pruned of any parts the asker could infer. Both directions share the same knowledge, so the architecture is reversible. The implemented system covers more kinds of indirect answers than earlier computational models. The authors argue that restricting logical inference avoids the scaling and cancellation problems of licensing-rule approaches to implicature.
Key points
- Stenström (1984) found that 13% of responses to Yes-No questions in spoken English were indirect. Extra information accompanied 61% of direct No answers and 24% of direct Yes answers.
- The top-level operators are Answer-yes, Answer-no, Answer-hedged, Answer-maybe and Answer-maybe-not. Satellites build on relations such as Condition, Elaboration, Cause, Contrast and Obstacle, where Obstacle is a non-RST relation close to RST's causal relations.
- During pruning, the generator tests frontier acts from right to left. It removes an act only if a simulated asker would still recover the full plan as the preferred reading.
- The system was written in Common Lisp and ran on a Sun SPARCstation, and beliefs are represented as Horn clauses.
Cited by
- Not a Simple Yes or No: Uncertainty in Indirect Answers
- Was It Good? It Was Provocative.” Learning the Meaning of Scalar Adjectives
Cite
@inproceedings{green1994hybrid,
title = {{A Hybrid Reasoning Model for Indirect Answers}},
author = {Green, Nancy and Carberry, Sandra},
booktitle = {32nd Annual Meeting of the Association for Computational Linguistics},
year = {1994},
doi = {10.3115/981732.981741},
eprint = {cmp-lg/9406014},
archivePrefix = {arXiv},
url = {https://doi.org/10.3115/981732.981741}
}References
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