论文标题
dyrren：用于表格和文本数据的数值推理的动态回收器速航器生成器模型
DyRRen: A Dynamic Retriever-Reranker-Generator Model for Numerical Reasoning over Tabular and Textual Data
论文作者
论文摘要
关于包含表和长文本的混合数据的数值推理最近受到了AI社区的研究关注。为了生成由数学和表操作组成的可执行推理程序来回答问题，最先进的方法使用了猎犬生成器管道。但是，他们的检索结果是静态的，而不同的一代步骤可能依赖于不同的句子。为了参与与每个一代步骤相关的检索信息，在本文中，我们提出了Dyrren，这是一个扩展的检索员速记员生成器框架，其中通过对检索到的句子的动态重新掌握了每个一代步骤。它的表现优于FinQA数据集上的现有基线。
Numerical reasoning over hybrid data containing tables and long texts has recently received research attention from the AI community. To generate an executable reasoning program consisting of math and table operations to answer a question, state-of-the-art methods use a retriever-generator pipeline. However, their retrieval results are static, while different generation steps may rely on different sentences. To attend to the retrieved information that is relevant to each generation step, in this paper, we propose DyRRen, an extended retriever-reranker-generator framework where each generation step is enhanced by a dynamic reranking of retrieved sentences. It outperforms existing baselines on the FinQA dataset.
