title: Multi-Task End-to-End Training Improves Conversational Recommendation
summary: 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.
paper_authors._id: 645c5cf4a7db8b98eb39ccbe
paper_authors.name: Naveen Ram
paper_authors._id: 645c5cf4a7db8b98eb39ccbf
paper_authors.name: Dima Kuzmin
paper_authors._id: 645c5cf4a7db8b98eb39ccc0
paper_authors.name: Ellie Ka In Chio
paper_authors._id: 645c5cf4a7db8b98eb39ccc1
paper_authors.name: Moustafa Farid Alzantot
paper_authors._id: 645c5cf4a7db8b98eb39ccc2
paper_authors.name: Santiago Ontanon
paper_authors._id: 645c5cf4a7db8b98eb39ccc3
paper_authors.name: Ambarish Jash
paper_authors._id: 645c5cf4a7db8b98eb39ccc4
paper_authors.name: Judith Yue Li
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-06-02T14:47:11.262Z
paper_authors._id: 5f431c9f79c1ba4c353d0d7f
paper_authors.avatarUrl: /avatars/7ac0936f469e63cfe8ce15849e128a43.svg
paper_authors.fullname: Judith Yue Li
paper_authors.type: user
paper_authors.user: whereisdodo
paper_title: Multi-Task End-to-End Training Improves Conversational Recommendation
paper_summary: 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.
paper_ai_summary: A unified transformer model, fine-tuned on conversational recommendations with additional multitask learning, achieves competitive performance in both item recommendation and dialogue generation.
paper_ai_keywords: transformer model multitask end-to-end T5 text-to-text conversational recommendations dialogue management entity recommendation ReDIAL MovieLens multitask learning probe studies