Paper Info
Title
Improving Neural Models for the Retrieval of Relevant Passages to Geographical Queries
Abstract
ABSTRACTPeople often ask questions about places, and this is reflected on the frequency of geo-spatial queries made to information retrieval and question answering systems. Recent developments associated to these two types of systems rely on deep neural networks, specifically on methods for passage retrieval based on Transformer models, trained on large datasets like MS-MARCO. Despite significant progress in approaches for retrieving (or re-ranking) passages from a document collection according to their relevance to an input query, few studies have specifically looked at geo-spatial queries (i.e., where-questions directly concerning locations, and also questions covering other informational needs relating to places, their types, and affordances). In this work, we explore neural retrieval models in the context of geo-spatial queries, using a subset of MS-MARCO with questions and passages containing place-names. After characterizing the subset of MS-MARCO, we analyzed a re-ranking strategy based on geographic distance, which we argue to be useful for selecting hard negative examples for model training. Then, we fine-tuned neural ranking models, following bi-encoder or cross-encoder strategies, using the MS-MARCO subset together with a geographically-aware negative sampling procedure. Experimental results show that the fine-tuned models can indeed achieve a superior performance. We also describe a simple knowledge distillation procedure to further improve the computationally more efficient bi-encoder models, using the results of the cross-encoder.
Year
DOI
Venue
2021
10.1145/3474717.3483960
Geographic Information Systems
DocType
Citations 
PageRank 
Conference
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
João Coelho100.34
João Magalhães200.34
Bruno Martins344134.58