  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.
