Geographic Question Answering: Challenges, Uniqueness, Classification, and Future Directions
arXiv:2105.09392
Abstract
As an important part of Artificial Intelligence (AI), Question Answering (QA) aims at generating answers to questions phrased in natural language. While there has been substantial progress in open-domain question answering, QA systems are still struggling to answer questions which involve geographic entities or concepts and that require spatial operations. In this paper, we discuss the problem of geographic question answering (GeoQA). We first investigate the reasons why geographic questions are difficult to answer by analyzing challenges of geographic questions. We discuss the uniqueness of geographic questions compared to general QA. Then we review existing work on GeoQA and classify them by the types of questions they can address. Based on this survey, we provide a generic classification framework for geographic questions. Finally, we conclude our work by pointing out unique future research directions for GeoQA.
arXiv comment20 pages, 3 figure, Full paper accepted to AGILE 2021
References in corpus (5)
- End-to-End Open-Domain Question Answering with BERTserini
- RSVQA: Visual Question Answering for Remote Sensing Data
- Memory Augmented Policy Optimization for Program Synthesis and Semantic Parsing
- Multi-Scale Representation Learning for Spatial Feature Distributions using Grid Cells
- Large Scale Question Answering using Tourism Data