@inproceedings{stoyanchev-etal-2022-combining,
title = "Combining Structured and Unstructured Knowledge in an Interactive Search Dialogue System",
author = "Stoyanchev, Svetlana and
Pandey, Suraj and
Keizer, Simon and
Braunschweiler, Norbert and
Doddipatla, Rama Sanand",
editor = "Lemon, Oliver and
Hakkani-Tur, Dilek and
Li, Junyi Jessy and
Ashrafzadeh, Arash and
Garcia, Daniel Hern{\'a}ndez and
Alikhani, Malihe and
Vandyke, David and
Du{\v{s}}ek, Ond{\v{r}}ej",
booktitle = "Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue",
month = sep,
year = "2022",
address = "Edinburgh, UK",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.sigdial-1.50",
doi = "10.18653/v1/2022.sigdial-1.50",
pages = "531--540",
abstract = "Users of interactive search dialogue systems specify their preferences with natural language utterances. However, a schema-driven system is limited to handling the preferences that correspond to the predefined database content. In this work, we present a methodology for extending a schema-driven interactive search dialogue system with the ability to handle unconstrained user preferences. Using unsupervised semantic similarity metrics and the text snippets associated with the search items, the system identifies suitable items for the user{'}s unconstrained natural language query. In crowd-sourced evaluation, the users chat with our extended restaurant search system. Based on objective metrics and subjective user ratings, we demonstrate the feasibility of using an unsupervised low latency approach to extend a schema-driven search dialogue system to handle unconstrained user preferences.",
}
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<abstract>Users of interactive search dialogue systems specify their preferences with natural language utterances. However, a schema-driven system is limited to handling the preferences that correspond to the predefined database content. In this work, we present a methodology for extending a schema-driven interactive search dialogue system with the ability to handle unconstrained user preferences. Using unsupervised semantic similarity metrics and the text snippets associated with the search items, the system identifies suitable items for the user’s unconstrained natural language query. In crowd-sourced evaluation, the users chat with our extended restaurant search system. Based on objective metrics and subjective user ratings, we demonstrate the feasibility of using an unsupervised low latency approach to extend a schema-driven search dialogue system to handle unconstrained user preferences.</abstract>
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%0 Conference Proceedings
%T Combining Structured and Unstructured Knowledge in an Interactive Search Dialogue System
%A Stoyanchev, Svetlana
%A Pandey, Suraj
%A Keizer, Simon
%A Braunschweiler, Norbert
%A Doddipatla, Rama Sanand
%Y Lemon, Oliver
%Y Hakkani-Tur, Dilek
%Y Li, Junyi Jessy
%Y Ashrafzadeh, Arash
%Y Garcia, Daniel Hernández
%Y Alikhani, Malihe
%Y Vandyke, David
%Y Dušek, Ondřej
%S Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue
%D 2022
%8 September
%I Association for Computational Linguistics
%C Edinburgh, UK
%F stoyanchev-etal-2022-combining
%X Users of interactive search dialogue systems specify their preferences with natural language utterances. However, a schema-driven system is limited to handling the preferences that correspond to the predefined database content. In this work, we present a methodology for extending a schema-driven interactive search dialogue system with the ability to handle unconstrained user preferences. Using unsupervised semantic similarity metrics and the text snippets associated with the search items, the system identifies suitable items for the user’s unconstrained natural language query. In crowd-sourced evaluation, the users chat with our extended restaurant search system. Based on objective metrics and subjective user ratings, we demonstrate the feasibility of using an unsupervised low latency approach to extend a schema-driven search dialogue system to handle unconstrained user preferences.
%R 10.18653/v1/2022.sigdial-1.50
%U https://aclanthology.org/2022.sigdial-1.50
%U https://doi.org/10.18653/v1/2022.sigdial-1.50
%P 531-540
Markdown (Informal)
[Combining Structured and Unstructured Knowledge in an Interactive Search Dialogue System](https://aclanthology.org/2022.sigdial-1.50) (Stoyanchev et al., SIGDIAL 2022)
ACL