论文标题
查询重写有效的错误信息发现
Query Rewriting for Effective Misinformation Discovery
论文作者
论文摘要
我们提出了一个新颖的系统，以帮助事实检查者为已知的误导性主张制定搜索查询，并在多个社交媒体平台上有效搜索。我们介绍了一种适应性的重写策略，其中编辑包含索赔的查询动作（例如，将单词与其同义词交换；将动词时态更改为当前简单），可以通过离线加强学习自动学习。我们的模型使用决策变压器来学习一系列编辑操作，以最大程度地提高查询检索指标，例如平均平均精度。我们进行了一系列实验，表明我们的查询重写系统可实现高达42％的查询有效性的相对提高，同时产生了可解释的编辑作用序列。
We propose a novel system to help fact-checkers formulate search queries for known misinformation claims and effectively search across multiple social media platforms. We introduce an adaptable rewriting strategy, where editing actions for queries containing claims (e.g., swap a word with its synonym; change verb tense into present simple) are automatically learned through offline reinforcement learning. Our model uses a decision transformer to learn a sequence of editing actions that maximizes query retrieval metrics such as mean average precision. We conduct a series of experiments showing that our query rewriting system achieves a relative increase in the effectiveness of the queries of up to 42%, while producing editing action sequences that are human interpretable.
