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%。

开放领域的会话问答的强大且高效的基线模型

Strong and Efficient Baselines for Open Domain Conversational Question  Answering

This paper introduces the second DIHARD challenge, the second in a series of
speaker diarization challenges intended to improve the robustness of
diarization systems to variation in recording equipment, noise conditions, and
conversational domain. The challenge comprises four tracks evaluating
diarization performance under two input conditions (single channel vs.
multi-channel) and two segmentation conditions (diarization from a reference
speech segmentation vs. diarization from scratch). In order to prevent
participants from overtuning to a particular combination of recording
conditions and conversational domain, recordings are drawn from a variety of
sources ranging from read audiobooks to meeting speech, to child language
acquisition recordings, to dinner parties, to web video. We describe the task
and metrics, challenge design, datasets, and baseline systems for speech
enhancement, speech activity detection, and diarization.

介绍第二届 DIHARD 挑战赛，该挑战赛旨在改善辨别系统对不同录音设备、噪音条件和对话领域变化的适应能力。本文描述了挑战任务、数据集和基线系统。