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.

翻译：本文分析了多任务端到端Transformer模型在对话推荐任务上的表现，该任务旨在基于用户在对话中明确表达的偏好提供推荐。尽管先前的研究采用复杂的多组件方法，其中对话管理和实体推荐任务由不同组件分别处理，但我们证明，基于T5文本到文本Transformer模型的统一Transformer架构能够在推荐相关项目与生成对话文本两方面均取得具有竞争力的性能。我们在ReDIAL对话式电影推荐数据集上微调模型，并在多任务学习框架下从MovieLens衍生出额外训练任务（例如基于输入电影预测电影属性及相关电影）。通过一系列探针研究，我们证实额外任务中习得的知识可迁移至对话场景，每项任务使其关联探针得分提升9%-52%。