Location Aware Modular Biencoder for Tourism Question Answering

Abstract

Question: Hi! My wife and I are in our late thirties and going to be in Dublin on September 28 and 29th. We are staying in the Grafton Street area. Does anybody have any suggestions for some fairly priced restaurants with great food within walking distance of Grafton Street? Also, What about some good pubs with live local music? (I realize it is a Sun and Mon night and may be slow) Any suggestion would be appreciated! Thanks! Answer ID: 11_R_4392 Answer Name: The Porterhouse Central, 45-47 Nassau Street, Dublin. Answering real-world tourism questions that seek Point-of-Interest (POI) recommendations is challenging, as it requires both spatial and non-spatial reasoning, over a large candidate pool. The traditional method of encoding each pair of question and POI becomes inefficient when the number of candidates increases, making it infeasible for real-world applications. To overcome this, we propose treating the QA task as a dense vector retrieval problem, where we encode questions and POIs separately and retrieve the most relevant POIs for a question by utilizing embedding space similarity. We use pretrained language models (PLMs) to encode textual information, and train a location encoder to capture spatial information of POIs. Experiments on a real-world tourism QA dataset demonstrate that our approach is effective, efficient, and outperforms previous methods across all metrics. Enabled by the dense retrieval architecture, we further build a global evaluation baseline, expanding the search space by 20 times compared to previous work. We also explore several factors that impact on the model’s performance through follow-up experiments. Our code and model are publicly available at https://github. com/haonan-li/LAMB. 1 Figure 1: An example of real-world POI recommendation question from the TourismQA dataset (Contractor et al., 2021b). Colored text represents constraints relevant to recommending POIs. or vague and ambiguous (e.g., within walking distance of Grafton Street) spatial constraints, as well as a fast indexing method that supports large-scale reasoning over both spatial and non-spatial (e.g., fairly priced restaurants) constraints. Recently, there has been increased interest in geospatial QA. Most approaches focus on querying structured knowledge bases, based on translating natural language questions into structured queries, e.g., using SPARQL (Punjani et al., 2018; Li et al., 2021; Hamzei et al., 2022). Separately, Contractor et al. (2021b) introduced the task of answering POIseeking questions using geospatial metadata and reviews that describe POIs. In later work, they proposed a spatial–textual reasoning network that uses distance-aware question embeddings as input and encodes question–POI pairs using attention (Contractor et al., 2021a). However, as their model creates separate question embeddings for each POI, the inference cost increases linearly in the number of POIs, and the model is incompatible with large