多任务端到端训练改进对话式推荐
Multi-Task End-to-End Training Improves Conversational Recommendation
AI总结：
本文提出基于T5的统一Transformer模型，通过多任务学习在ReDIAL数据集上微调，并利用MovieLens派生任务，实现对话式推荐中推荐与对话生成的竞争力，探针得分提升9%-52%。
摘要：
在本文中，我们分析了多任务端到端Transformer模型在对话式推荐任务上的性能，该任务旨在根据用户在对话中表达的明确偏好提供推荐。虽然该领域先前的工作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的组件处理，但我们证明，基于T5文本到文本Transformer模型的统一Transformer模型，在推荐相关项目和生成对话文本方面均能表现出竞争力。我们在ReDIAL对话式电影推荐数据集上对模型进行微调，并在多任务学习设置中创建了源自MovieLens的额外训练任务（例如，基于输入电影预测电影属性和相关电影）。通过一系列探针研究，我们证明了额外任务中学到的知识被迁移到对话场景中，每个任务使其相关探针得分提高了9%至52%。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
