  A user input to a schema-driven dialogue information navigation system, such
as venue search, is typically constrained by the underlying database which
restricts the user to specify a predefined set of preferences, or slots,
corresponding to the database fields. We envision a more natural information
navigation dialogue interface where a user has flexibility to specify
unconstrained preferences that may not match a predefined schema. We propose to
use information retrieval from unstructured knowledge to identify entities
relevant to a user request. We update the Cambridge restaurants database with
unstructured knowledge snippets (reviews and information from the web) for each
of the restaurants and annotate a set of query-snippet pairs with a relevance
label. We use the annotated dataset to train and evaluate snippet relevance
classifiers, as a proxy to evaluating recommendation accuracy. We show that
with a pretrained transformer model as an encoder, an unsupervised/supervised
classifier achieves a weighted F1 of .661/.856.
