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.

本文提出了一种名为 DyRRen 的扩展检索 - 筛选 - 生成（retriever-reranker-generator）框架，其中每个生成步骤都通过动态的筛选和重新排序所检索到的句子来增强，可以应用于数值推理和融合表格与长文本的 AI 任务中，并在 FinQA 数据集上胜过现有的基线方法。