MGeo: Multi-Modal Geographic Language Model Pre-Training

Query and point of interest (POI) matching is a core task in location-based services (LBS), e.g., navigation maps. It connects users' intent with real-world geographic information. Lately, pre-trained language models (PLMs) have made notable advancements in many natural language processing (NLP) tasks. To overcome the limitation that generic PLMs lack geographic knowledge for query-POI matching, related literature attempts to employ continued pre-training based on domain-specific corpus. However, a query generally describes the geographic context (GC) about its destination and contains mentions of multiple geographic objects like nearby roads and regions of interest (ROIs). These diverse geographic objects and their correlations are pivotal to retrieving the most relevant POI. Text-based single-modal PLMs can barely make use of the important GC and are therefore limited. In this work, we propose a novel method for query-POI matching, namely Multi-modal Geographic language model (MGeo), which comprises a geographic encoder and a multi-modal interaction module. Representing GC as a new modality, MGeo is able to fully extract multi-modal correlations to perform accurate query-POI matching. Moreover, there exists no publicly available query-POI matching benchmark. Intending to facilitate further research, we build a new open-source large-scale benchmark for this topic, i.e., Geographic TExtual Similarity (GeoTES). The POIs come from an open-source geographic information system (GIS) and the queries are manually generated by annotators to prevent privacy issues. Compared with several strong baselines, the extensive experiment results and detailed ablation analyses demonstrate that our proposed multi-modal geographic pre-training method can significantly improve the query-POI matching capability of PLMs with or without users' locations. Our code and benchmark are publicly available at https://github.com/PhantomGrapes/MGeo.