Exploring Rewriting Approaches for Different Conversational Tasks

• URL: http://arxiv.org/abs/2502.18860v2
• Date: Fri, 28 Feb 2025 04:18:19 GMT
• Title: Exploring Rewriting Approaches for Different Conversational Tasks
• Authors: Md Mehrab Tanjim, Ryan A. Rossi, Mike Rimer, Xiang Chen, Sungchul Kim, Vaishnavi Muppala, Tong Yu, Zhengmian Hu, Ritwik Sinha, Wei Zhang, Iftikhar Ahamath Burhanuddin, Franck Dernoncourt,
• Abstract summary: The exact rewriting approach may often depend on the use case and application-specific tasks supported by the conversational assistant.<n>We systematically investigate two different approaches, denoted as rewriting and fusion, on two fundamentally different generation tasks.<n>Our results indicate that the specific rewriting or fusion approach highly depends on the underlying use case and generative task.
• Score: 63.56404271441824
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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