Open-Domain Conversational Question Answering (ODConvQA) aims at answering questions through a multi-turn conversation based on a retriever-reader pipeline, which retrieves passages and then predicts answers with them. However, such a pipeline approach not only makes the reader vulnerable to the errors propagated from the retriever, but also demands additional effort to develop both the retriever and the reader, which further makes it slower since they are not runnable in parallel. In this work, we propose a method to directly predict answers with a phrase retrieval scheme for a sequence of words, reducing the conventional two distinct subtasks into a single one. Also, for the first time, we study its capability for ODConvQA tasks. However, simply adopting it is largely problematic, due to the dependencies between previous and current turns in a conversation. To address this problem, we further introduce a novel contrastive learning strategy, making sure to reflect previous turns when retrieving the phrase for the current context, by maximizing representational similarities of consecutive turns in a conversation while minimizing irrelevant conversational contexts. We validate our model on two ODConvQA datasets, whose experimental results show that it substantially outperforms the relevant baselines with the retriever-reader. Code is available at: https://github.com/starsuzi/PRO-ConvQA.

翻译：开放域对话式问答（ODConvQA）旨在基于检索-阅读器流水线，通过多轮对话回答问题，即先检索段落再利用其预测答案。然而，这种流水线方法不仅使阅读器易受检索器错误传播的影响，还需额外开发检索器和阅读器，且因二者无法并行运行而降低效率。本文提出一种方法，通过针对词序列的短语检索方案直接预测答案，将传统的两个不同子任务简化为单一任务。同时，我们首次研究该方法在ODConvQA任务中的能力。然而，由于对话中前后轮次之间存在依赖关系，直接采用该方案会引发诸多问题。为解决此问题，我们进一步引入一种新颖的对比学习策略，通过最大化对话中连续轮次的表征相似性并最小化无关对话上下文，确保在检索当前上下文的短语时反映先前轮次的信息。我们在两个ODConvQA数据集上验证了模型，实验结果表明，该方法显著优于基于检索-阅读器的相关基线。代码地址：https://github.com/starsuzi/PRO-ConvQA。