Unlike the Open Domain Question Answering (ODQA) setting, the conversational
(ODConvQA) domain has received limited attention when it comes to reevaluating
baselines for both efficiency and effectiveness. In this paper, we study the
State-of-the-Art (SotA) Dense Passage Retrieval (DPR) retriever and
Fusion-in-Decoder (FiD) reader pipeline, and show that it significantly
underperforms when applied to ODConvQA tasks due to various limitations. We
then propose and evaluate strong yet simple and efficient baselines, by
introducing a fast reranking component between the retriever and the reader,
and by performing targeted finetuning steps. Experiments on two ODConvQA tasks,
namely TopiOCQA and OR-QuAC, show that our method improves the SotA results,
while reducing reader's latency by 60%. Finally, we provide new and valuable
insights into the development of challenging baselines that serve as a
reference for future, more intricate approaches, including those that leverage
Large Language Models (LLMs).

通过研究状况下的密集通道检索（DPR）检索器和解码中的融合（FiD）阅读器的流程，我们提出和评估了强大而简单高效的基准线，通过在检索器和阅读器之间引入快速重新排列组件并执行有针对性的微调步骤，改进了现有方法的性能，并减少了阅读器的延迟 60%。