In conversational question answering, systems must correctly interpret the
interconnected interactions and generate knowledgeable answers, which may
require the retrieval of relevant information from a background repository.
Recent approaches to this problem leverage neural language models, although
different alternatives can be considered in terms of modules for (a)
representing user questions in context, (b) retrieving the relevant background
information, and (c) generating the answer. This work presents a conversational
question answering system designed specifically for the Search-Oriented
Conversational AI (SCAI) shared task, and reports on a detailed analysis of its
question rewriting module. In particular, we considered different variations of
the question rewriting module to evaluate the influence on the subsequent
components, and performed a careful analysis of the results obtained with the
best system configuration. Our system achieved the best performance in the
shared task and our analysis emphasizes the importance of the conversation
context representation for the overall system performance.

本文研究了基于神经语言模型、以及不同的信息提取和答案生成方式，在特定的搜索导向的对话人工智能任务中，如何通过代表问题上下文的不同重写模块，从背景知识库中提取信息，生成知识回答，最终取得了最佳表现，并在分析中强调了对话上下文的重要性。