Multi-Task End-to-End Training Improves Conversational Recommendation
In this paper, we analyze the performance of a multitask end-to-end
transformer model on the task of conversational recommendations, which aim to
provide recommendations based on a user's explicit preferences expressed in
dialogue. While previous works in this area adopt complex multi-component
approaches where the dialogue management and entity recommendation tasks are
handled by separate components, we show that a unified transformer model, based
on the T5 text-to-text transformer model, can perform competitively in both
recommending relevant items and generating conversation dialogue. We fine-tune
our model on the ReDIAL conversational movie recommendation dataset, and create
additional training tasks derived from MovieLens (such as the prediction of
movie attributes and related movies based on an input movie), in a multitask
learning setting. Using a series of probe studies, we demonstrate that the
learned knowledge in the additional tasks is transferred to the conversational
setting, where each task leads to a 9%-52% increase in its related probe score.
645c5cf4a7db8b98eb39ccbe
Naveen Ram
645c5cf4a7db8b98eb39ccbf
Dima Kuzmin
645c5cf4a7db8b98eb39ccc0
Ellie Ka In Chio
645c5cf4a7db8b98eb39ccc1
Moustafa Farid Alzantot
645c5cf4a7db8b98eb39ccc2
Santiago Ontanon
645c5cf4a7db8b98eb39ccc3
Ambarish Jash
645c5cf4a7db8b98eb39ccc4
Judith Yue Li
admin_assigned
2023-06-02T14:47:11.262Z
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/avatars/7ac0936f469e63cfe8ce15849e128a43.svg
Judith Yue Li
user
whereisdodo
Multi-Task End-to-End Training Improves Conversational Recommendation
In this paper, we analyze the performance of a multitask end-to-end
transformer model on the task of conversational recommendations, which aim to
provide recommendations based on a user's explicit preferences expressed in
dialogue. While previous works in this area adopt complex multi-component
approaches where the dialogue management and entity recommendation tasks are
handled by separate components, we show that a unified transformer model, based
on the T5 text-to-text transformer model, can perform competitively in both
recommending relevant items and generating conversation dialogue. We fine-tune
our model on the ReDIAL conversational movie recommendation dataset, and create
additional training tasks derived from MovieLens (such as the prediction of
movie attributes and related movies based on an input movie), in a multitask
learning setting. Using a series of probe studies, we demonstrate that the
learned knowledge in the additional tasks is transferred to the conversational
setting, where each task leads to a 9%-52% increase in its related probe score.
A unified transformer model, fine-tuned on conversational recommendations with additional multitask learning, achieves competitive performance in both item recommendation and dialogue generation.
transformer model multitask end-to-end T5 text-to-text conversational recommendations dialogue management entity recommendation ReDIAL MovieLens multitask learning probe studies