Geo-Encoder: A Chunk-Argument Bi-Encoder Framework for Chinese Geographic Re-Ranking
Conference of the European Chapter of the Association for Computational Linguistics(2023)
摘要
Chinese geographic re-ranking task aims to find the most relevant addresses
among retrieved candidates, which is crucial for location-related services such
as navigation maps. Unlike the general sentences, geographic contexts are
closely intertwined with geographical concepts, from general spans (e.g.,
province) to specific spans (e.g., road). Given this feature, we propose an
innovative framework, namely Geo-Encoder, to more effectively integrate Chinese
geographical semantics into re-ranking pipelines. Our methodology begins by
employing off-the-shelf tools to associate text with geographical spans,
treating them as chunking units. Then, we present a multi-task learning module
to simultaneously acquire an effective attention matrix that determines chunk
contributions to extra semantic representations. Furthermore, we put forth an
asynchronous update mechanism for the proposed addition task, aiming to guide
the model capable of effectively focusing on specific chunks. Experiments on
two distinct Chinese geographic re-ranking datasets, show that the Geo-Encoder
achieves significant improvements when compared to state-of-the-art baselines.
Notably, it leads to a substantial improvement in the Hit@1 score of MGEO-BERT,
increasing it by 6.22
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要