Question rewriting? Assessing its importance for conversational question answering
This work addresses conversational question answering for AI systems, but it is incremental as it builds on existing neural language models and focuses on module variations within a specific shared task.
The paper tackled the problem of conversational question answering by developing a system for the SCAI shared task, focusing on analyzing its question rewriting module, and it achieved the best performance in the task, emphasizing the importance of conversation context representation.
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.