Using explanation-based learning to increase performance in a large-scale NL query system
HLT '90 Proceedings of the workshop on Speech and Natural Language
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 2
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Explanation-based learning is a technique which attempts to optimiz performance of a rule-based system by adding new rules constructed from generalizations of successfully-solved examples. The paper summarizes previous work showing how this idea can be used in natural language processing, and describes experiments in which the EBL method was applied to the CHAT-80 system of Pereira and Warren. In particular, we address the problem of assuring the utility of learning a rule, since the benefit of a learned rule may not outweigh the increased search time incurred in checking its applicability. We show that this problem can be overcome in the NL domain by indexing acquired rules by their lexical constraints, which in general vastly reduces the number of potentially applicable rules. Such an indexing method was implemented and timing studies were made comparing its access speed to that of normal linear search. The indexing scheme required an average access time of 35 - 40 ms independent of the number of learned rules. The results suggest that the overhead of the indexing scheme is small.