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Answering real-world tourism questions that seek Point-of-Interest (POI)recommendations is challenging, as it requires both spatial and non-spatialreasoning, over a large candidate pool. The traditional method of encoding eachpair of question and POI becomes inefficient when the number of candidatesincreases, making it infeasible for real-world applications. To overcome this,we propose treating the QA task as a dense vector retrieval problem, where weencode questions and POIs separately and retrieve the most relevant POIs for aquestion by utilizing embedding space similarity. We use pretrained languagemodels (PLMs) to encode textual information, and train a location encoder tocapture spatial information of POIs. Experiments on a real-world tourism QAdataset demonstrate that our approach is effective, efficient, and outperformsprevious methods across all metrics. Enabled by the dense retrievalarchitecture, we further build a global evaluation baseline, expanding thesearch space by 20 times compared to previous work. We also explore severalfactors 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.

Location Aware Modular Biencoder for Tourism Question Answering