Exploring Rewriting Approaches for Different Conversational Tasks
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.
67c95acf88c3b4201c10b9e9
Md Mehrab Tanjim
67c95acf88c3b4201c10b9ea
Ryan A. Rossi
67c95acf88c3b4201c10b9eb
Mike Rimer
67c95acf88c3b4201c10b9ec
Xiang Chen
67c95acf88c3b4201c10b9ed
Sungchul Kim
67c95acf88c3b4201c10b9ee
Vaishnavi Muppala
67c95acf88c3b4201c10b9ef
Tong Yu
67c95acf88c3b4201c10b9f0
Zhengmian Hu
67c95acf88c3b4201c10b9f1
Ritwik Sinha
67c95acf88c3b4201c10b9f2
Wei Zhang
67c95acf88c3b4201c10b9f3
Iftikhar Ahamath Burhanuddin
67c95acf88c3b4201c10b9f4
Franck Dernoncourt
claimed_verified
2025-03-06T09:24:24.968Z
62c5947524171688a9feb992
/avatars/5a151713b9eae8dc566f5957acee3475.svg
Franck Dernoncourt
user
Franck-Dernoncourt
Exploring Rewriting Approaches for Different Conversational Tasks
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.
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.
question rewriting fusion text-to-text generation multimodal generative task query rewriting query fusion conversational question-answering assistant data analysis assistant