探索不同对话任务中的改写方法

摘要

对话助手通常需要一种问题重写算法，该算法利用过往交互的子集来为用户的问题或请求提供更准确、更有意义的回答。然而，具体的重写策略往往取决于对话助手所支持的用例和应用场景任务，以及其他限制条件。本文中，我们系统性地探讨了两种不同的方法，即重写与融合，应用于两种本质不同的生成任务：包括一个文本到文本的生成任务，以及一个多模态生成任务，后者以文本为输入并生成可视化图表或数据表来回答用户问题。我们的研究结果表明，选择重写还是融合方法高度依赖于具体的应用场景和生成任务。特别是，我们发现对于基于对话的问答助手，查询重写方法表现最佳；而对于根据用户与助手的对话生成可视化图表和数据表的数据分析助手，融合方法效果更优。值得注意的是，我们针对数据分析助手的用例探索了两个数据集，分别对应短对话和长对话，发现查询融合方法始终表现更佳，而在基于文本的对话问答场景中，查询重写方法则最为有效。

English

Conversational assistants often require a question rewriting algorithm that leverages a subset of past interactions to provide a more meaningful (accurate) answer to the user's question or request. However, the exact rewriting approach may often depend on the use case and application-specific tasks supported by the conversational assistant, among other constraints. In this paper, we systematically investigate two different approaches, denoted as rewriting and fusion, on two fundamentally different generation tasks, including a text-to-text generation task and a multimodal generative task that takes as input text and generates a visualization or data table that answers the user's question. Our results indicate that the specific rewriting or fusion approach highly depends on the underlying use case and generative task. In particular, we find that for a conversational question-answering assistant, the query rewriting approach performs best, whereas for a data analysis assistant that generates visualizations and data tables based on the user's conversation with the assistant, the fusion approach works best. Notably, we explore two datasets for the data analysis assistant use case, for short and long conversations, and we find that query fusion always performs better, whereas for the conversational text-based question-answering, the query rewrite approach performs best.

探索不同对话任务中的改写方法

Exploring Rewriting Approaches for Different Conversational Tasks

摘要

Summary

Support