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.

翻译：回答现实世界中寻求兴趣点推荐的旅游问题具有挑战性，因为它需要在大量候选集中同时进行空间和非空间推理。当候选集数量增加时，传统方法对每个问题和兴趣点对进行编码的效率会降低，使得其难以应用于真实场景。为解决这一问题，我们提出将问答任务视为密集向量检索问题，分别对问题和兴趣点进行编码，并通过嵌入空间相似度检索与问题最相关的兴趣点。我们使用预训练语言模型编码文本信息，并训练位置编码器以捕获兴趣点的空间信息。在真实旅游问答数据集上的实验表明，我们的方法有效且高效，在所有指标上均优于此前的方法。借助密集检索架构，我们进一步构建了全局评估基线，将搜索空间扩展至此前工作的20倍。我们还通过后续实验探索了影响模型性能的若干因素。我们的代码和模型已在https://github.com/haonan-li/LAMB公开。