改进开放域对话系统的多任务学习实证研究
An Empirical Study of Multitask Learning to Improve Open Domain Dialogue Systems
AI总结：
本文通过在GPT-2解码器模型上引入四种辅助任务进行多任务学习，验证其能小幅一致地提升开放域对话系统在PersonaChat和DailyDialog上的表现。
摘要：
用于在开放域对话系统中生成响应的自回归模型往往难以考虑长期上下文，也难以在对话过程中保持一致性。先前关于开放域对话生成的研究表明，使用辅助任务可以引入归纳偏置，促使模型改善这些特性。然而，大多数先前的研究集中于仅编码器或编码器-解码器模型，而在仅解码器自回归模型中使用辅助任务的研究尚不充分。本文描述了一项研究，将四种不同的辅助任务添加到在PersonaChat和DailyDialog数据集上微调的小型和中等规模GPT-2模型中。结果表明，引入新的辅助任务在所研究模型的评估中带来了小幅但一致的改进。
英文摘要：
Autoregressive models used to generate responses in open-domain dialogue systems often struggle to take long-term context into account and to maintain consistency over a dialogue. Previous research in open-domain dialogue generation has shown that the use of \emph{auxiliary tasks} can introduce inductive biases that encourage the model to improve these qualities. However, most previous research has focused on encoder-only or encoder/decoder models, while the use of auxiliary tasks in \emph{decoder-only} autoregressive models is under-explored. This paper describes an investigation where four different auxiliary tasks are added to small and medium-sized GPT-2 models fine-tuned on the PersonaChat and DailyDialog datasets. The results show that the introduction of the new auxiliary tasks leads to small but consistent improvement in evaluations of the investigated models.
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。
