Exploring Rewriting Approaches for Different Conversational Tasks
Authors: name: Md Mehrab Tanjim, name: Ryan A. Rossi, name: Mike Rimer, name: Xiang Chen, name: Sungchul Kim, name: Vaishnavi Muppala, name: Tong Yu, name: Zhengmian Hu, name: Ritwik Sinha, name: Wei Zhang, name: Iftikhar Ahamath Burhanuddin, name: Franck Dernoncourt

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.