Location Aware Modular Biencoder for Tourism Question Answering

• URL: http://arxiv.org/abs/2401.02187v1
• Date: Thu, 4 Jan 2024 10:39:58 GMT
• Title: Location Aware Modular Biencoder for Tourism Question Answering
• Authors: Haonan Li, Martin Tomko, Timothy Baldwin
• Abstract summary: We propose treating the QA task as a dense vector retrieval problem. We encode questions and POIs separately and retrieve the most relevant POIs for a question by utilizing embedding space similarity. Experiments on a real-world tourism QA dataset demonstrate that our approach is effective, efficient, and outperforms previous methods.
• Score: 33.5507972300392
• License: http://creativecommons.org/licenses/by/4.0/
• 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 the dense retrieval architecture, we further build a global evaluation baseline, expanding the search space by 20 times compared to previous work. We also explore several factors that impact on the model's performance through follow-up experiments. Our code and model are publicly available at https://github.com/haonan-li/LAMB.

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