ERNIE-GeoL: A Geography-and-Language Pre-trained Model and its Applications in Baidu Maps
This addresses the lack of geographic knowledge in pre-trained models for geo-related applications at Baidu Maps, representing an incremental domain-specific advancement.
The authors tackled the performance plateau in geo-related tasks at Baidu Maps by developing ERNIE-GeoL, a geography-and-language pre-trained model, which significantly improved downstream task performance and has been deployed in production since April 2021.
Pre-trained models (PTMs) have become a fundamental backbone for downstream tasks in natural language processing and computer vision. Despite initial gains that were obtained by applying generic PTMs to geo-related tasks at Baidu Maps, a clear performance plateau over time was observed. One of the main reasons for this plateau is the lack of readily available geographic knowledge in generic PTMs. To address this problem, in this paper, we present ERNIE-GeoL, which is a geography-and-language pre-trained model designed and developed for improving the geo-related tasks at Baidu Maps. ERNIE-GeoL is elaborately designed to learn a universal representation of geography-language by pre-training on large-scale data generated from a heterogeneous graph that contains abundant geographic knowledge. Extensive quantitative and qualitative experiments conducted on large-scale real-world datasets demonstrate the superiority and effectiveness of ERNIE-GeoL. ERNIE-GeoL has already been deployed in production at Baidu Maps since April 2021, which significantly benefits the performance of various downstream tasks. This demonstrates that ERNIE-GeoL can serve as a fundamental backbone for a wide range of geo-related tasks.