Location Aware Modular Biencoder for Tourism Question Answering
Haonan Li, Martin Tomko, Timothy Baldwin

TL;DR
This paper introduces a location-aware modular biencoder that encodes questions and POIs separately for efficient tourism question answering, leveraging dense vector retrieval and spatial reasoning to outperform previous methods.
Contribution
It presents a novel modular biencoder architecture with separate encoders for questions and POIs, improving efficiency and scalability in tourism QA tasks.
Findings
Outperforms previous methods across all metrics
Enables search space expansion by 20 times
Effective use of pretrained language models and spatial encoding
Abstract
Answering real-world tourism questions that seek Point-of-Interest (POI) recommendations is challenging, as it requires both spatial and non-spatial reasoning, over a large candidate pool. The traditional method of encoding each pair of question and POI becomes inefficient when the number of candidates increases, making it infeasible for real-world applications. To overcome this, we propose treating the QA task as a dense vector retrieval problem, where we encode questions and POIs separately and retrieve the most relevant POIs for a question by utilizing embedding space similarity. We use pretrained language models (PLMs) to encode textual information, and train a location encoder to capture spatial information of POIs. Experiments on a real-world tourism QA dataset demonstrate that our approach is effective, efficient, and outperforms previous methods across all metrics. Enabled by…
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Taxonomy
TopicsMultimodal Machine Learning Applications · Topic Modeling · Expert finding and Q&A systems
