面向旅游问答的位置感知模块化双编码器
Location Aware Modular Biencoder for Tourism Question Answering
AI总结：
针对旅游问答中POI推荐的空间与非空间推理挑战，提出基于稠密向量检索的模块化双编码器方法，分别编码问题与POI，在真实数据集上超越基线并将搜索空间扩大20倍。
摘要：
回答寻求兴趣点（POI）推荐的现实世界旅游问题具有挑战性，因为它需要在庞大的候选池上进行空间和非空间推理。当候选数量增加时，对每个问题与POI对进行编码的传统方法效率低下，使其在现实应用中不可行。为克服这一点，我们提出将QA任务视为稠密向量检索问题，分别编码问题和POI，并利用嵌入空间相似度检索与问题最相关的POI。我们使用预训练语言模型（PLM）编码文本信息，并训练一个位置编码器以捕捉POI的空间信息。在真实旅游QA数据集上的实验表明，我们的方法有效且高效，并在所有指标上超越了先前的方法。得益于稠密检索架构，我们进一步构建了全局评估基线，将搜索空间相比先前工作扩大了20倍。我们还通过后续实验探索了影响模型性能的几个因素。代码和模型公开于https://github.com/haonan-li/LAMB。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
