Multi-Task Learning of Query Generation and Classification for Generative Conversational Question Rewriting

Sarawoot Kongyoung, Craig MacDonald, Iadh Ounis

Abstract:

In conversational search settings, users ask
questions and receive answers as part of a conversation. The ambiguity in the questions is a
common challenge, which can be effectively
addressed by leveraging contextual information
from the conversation history. In this context,
determining topic continuity and reformulating
questions into well-defined queries are crucial
tasks. Previous approaches have typically addressed these tasks either as a classification task
in the case of topic continuity or as a text generation task for question reformulation. However,
no prior work has combined both tasks to effectively identify ambiguous questions as part of a
conversation. In this paper, we propose a MultiTask Learning (MTL) approach that uses a text
generation model for both question rewriting
and classification. Our models, based on BART
and T5, are trained to rewrite conversational
questions and identify follow-up questions simultaneously. We evaluate our approach on
multiple test sets and demonstrate that it outperforms single-task learning baselines on the
three LIF test sets, with statistically significant
improvements ranging from +3.5% to +10.5%
in terms of F1 and Micro-F1 scores. We also
show that our approach outperforms single-task
question rewriting models in passage retrieval
on a large OR-QuAC test set.