Joint Spatio-Textual Reasoning for Answering Tourism Questions
This addresses the need for better POI recommendations in tourism by combining spatial and textual constraints, though it appears incremental as it builds on existing reasoning methods.
The paper tackles the problem of answering tourism questions that require Points-of-Interest recommendations by developing a joint spatio-textual reasoning model, reporting substantial improvements over existing models without such joint reasoning.
Our goal is to answer real-world tourism questions that seek Points-of-Interest (POI) recommendations. Such questions express various kinds of spatial and non-spatial constraints, necessitating a combination of textual and spatial reasoning. In response, we develop the first joint spatio-textual reasoning model, which combines geo-spatial knowledge with information in textual corpora to answer questions. We first develop a modular spatial-reasoning network that uses geo-coordinates of location names mentioned in a question, and of candidate answer POIs, to reason over only spatial constraints. We then combine our spatial-reasoner with a textual reasoner in a joint model and present experiments on a real world POI recommendation task. We report substantial improvements over existing models with-out joint spatio-textual reasoning.