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.

翻译：我们提出一种新系统，旨在帮助事实核查员针对已知虚假信息主张构建搜索查询，并跨多个社交媒体平台进行高效检索。我们引入一种可适应的重写策略，其中包含主张的查询的编辑操作（例如，将词语替换为同义词；将动词时态改为一般现在时）通过离线强化学习自动习得。我们的模型采用决策变换器学习一系列编辑操作，以最大化查询检索指标（如平均准确率均值）。通过一系列实验，我们证明该查询重写系统能使查询有效性相对提升高达42%，同时生成的编辑操作序列具有人类可解释性。