论文标题
多观点语义信息检索
Multi-Perspective Semantic Information Retrieval
论文作者
论文摘要
信息检索（ir）是获得与特定查询或从大量信息存储库中相关的数据（例如文档或文本片段）的任务。尽管已经证明传统的关键字和现代基于BERT的方法在最近的工作中有效，但通常在确定哪些信息与特定查询“相关”通常存在细微差别，而这些查询可能很难使用这些系统正确捕获。这项工作介绍了多个IR系统的概念，一种新颖的方法结合了多个深度学习和传统的IR模型，以更好地预测查询句子对的相关性，以及用于调整该系统的标准化框架。这项工作是在Bioasq生物医学IR + QA挑战上评估的。
Information Retrieval (IR) is the task of obtaining pieces of data (such as documents or snippets of text) that are relevant to a particular query or need from a large repository of information. While a combination of traditional keyword- and modern BERT-based approaches have been shown to be effective in recent work, there are often nuances in identifying what information is "relevant" to a particular query, which can be difficult to properly capture using these systems. This work introduces the concept of a Multi-Perspective IR system, a novel methodology that combines multiple deep learning and traditional IR models to better predict the relevance of a query-sentence pair, along with a standardized framework for tuning this system. This work is evaluated on the BioASQ Biomedical IR + QA challenges.
