title: Exploring Rewriting Approaches for Different Conversational Tasks
summary: 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.
paper_authors._id: 67c95acf88c3b4201c10b9e9
paper_authors.name: Md Mehrab Tanjim
paper_authors._id: 67c95acf88c3b4201c10b9ea
paper_authors.name: Ryan A. Rossi
paper_authors._id: 67c95acf88c3b4201c10b9eb
paper_authors.name: Mike Rimer
paper_authors._id: 67c95acf88c3b4201c10b9ec
paper_authors.name: Xiang Chen
paper_authors._id: 67c95acf88c3b4201c10b9ed
paper_authors.name: Sungchul Kim
paper_authors._id: 67c95acf88c3b4201c10b9ee
paper_authors.name: Vaishnavi Muppala
paper_authors._id: 67c95acf88c3b4201c10b9ef
paper_authors.name: Tong Yu
paper_authors._id: 67c95acf88c3b4201c10b9f0
paper_authors.name: Zhengmian Hu
paper_authors._id: 67c95acf88c3b4201c10b9f1
paper_authors.name: Ritwik Sinha
paper_authors._id: 67c95acf88c3b4201c10b9f2
paper_authors.name: Wei Zhang
paper_authors._id: 67c95acf88c3b4201c10b9f3
paper_authors.name: Iftikhar Ahamath Burhanuddin
paper_authors._id: 67c95acf88c3b4201c10b9f4
paper_authors.name: Franck Dernoncourt
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-06T09:24:24.968Z
paper_authors._id: 62c5947524171688a9feb992
paper_authors.avatarUrl: /avatars/5a151713b9eae8dc566f5957acee3475.svg
paper_authors.fullname: Franck Dernoncourt
paper_authors.type: user
paper_authors.user: Franck-Dernoncourt
paper_title: Exploring Rewriting Approaches for Different Conversational Tasks
paper_summary: 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.
paper_ai_summary: Two question rewriting approaches, rewriting and fusion, are investigated for conversational assistants, showing that rewriting excels for question-answering while fusion is better for generating visualizations and data tables based on user conversations.
paper_ai_keywords: question rewriting fusion text-to-text generation multimodal generative task query rewriting query fusion conversational question-answering assistant data analysis assistant