Learning to Generate Naturalistic Utterances Using Reviews in Spoken Dialogue Systems

Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the ACL, pages 265–272, Sydney, July 2006. c⃝2006 Association for Computational Linguistics Learning to Generate Naturalistic Utterances Using Reviews in Spoken Dialogue Systems Ryuichiro Higashinaka NTT Corporation rh@cslab.kecl.ntt.co.jp Rashmi Prasad University of Pennsylvania rjprasad@linc.cis.upenn.edu Marilyn A. Walker University of Sheffield walker@dcs.shef.ac.uk Abstract Spoken language generation for dialogue systems requires a dictionary of mappings between semantic representations of con- cepts the system wants to express and re- alizations of those concepts. Dictionary creation is a costly process; it is currently done by hand for each dialogue domain. We propose a novel unsupervised method for learning such mappings from user re- views in the target domain, and test it on restaurant reviews. We test the hypothesis that user reviews that provide individual ratings for distinguished attributes of the domain entity make it possible to map re- view sentences to their semantic represen- tation with high precision. Experimental analyses show that the mappings learned cover most of the domain ontology, and provide good linguistic variation. A sub- jective user evaluation shows that the con- sistency between the semantic representa- tions and the learned realizations is high and that the naturalness of the realizations is higher than a hand-crafted baseline. 1 Introduction One obstacle to the widespread deployment of spoken dialogue systems is the cost involved with hand-crafting the spoken language generation module. Spoken language generation requires a dictionary of mappings between semantic repre- sentations of concepts the system wants to express and realizations of those concepts. Dictionary cre- ation is a costly process: an automatic method for creating them would make dialogue technol- ogy more scalable. A secondary benefit is that a learned dictionary may produce more natural and colloquial utterances. We propose a novel method for mining user re- views to automatically acquire a domain specific generation dictionary for information presentation in a dialogue system. Our hypothesis is that re- views that provide individual ratings for various distinguished attributes of review entities can be used to map review sentences to a semantic rep- An example user review (we8there.com) Ratings Food=5, Service=5, Atmosphere=5, Value=5, Overall=5 Review comment The best Spanish food in New York. I am from Spain and I had my 28th birthday there and we all had a great time. Salud! ↓ Review comment after named entity recognition The best {NE=foodtype, string=Spanish} {NE=food, string=food, rating=5} in {NE=location, string=New York}. . . . ↓ Mapping between a semantic representation (a set of relations) and a syntactic structure (DSyntS) • Relations: RESTAURANT has FOODTYPE RESTAURANT has foodquality=5 RESTAURANT has LOCATION ([foodtype, food=5, location] for shorthand.) • DSyntS: ⎡ ⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣ lexeme : food class : common noun number : sg article : def ATTR � lexeme : best class : adjective � ATTR ⎡ ⎣ lexeme : FOODTYPE class : common noun number : sg article : no-art ⎤ ⎦ ATTR ⎡ ⎢⎢⎢⎣ lexeme : in class : preposition II ⎡ ⎣ lexeme : LOCATION class : proper noun number …

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