论文标题
AnyTOD：可编程的以任务为导向的对话框系统
AnyTOD: A Programmable Task-Oriented Dialog System
论文作者
论文摘要
我们提出了AnyTOD，即端到端，零射击任务的对话框（TOD）系统，能够处理未见任务而没有特定任务的培训。我们将TOD视为由语言模型（LM）执行的程序，在该程序中，设计人员将程序逻辑和本体论作为架构提供。为了使概括在没有事先培训的情况下看不见的模式和程序，AnyTOD采用了神经符号方法。神经LM跟踪对话期间发生的事件，并执行实施对话策略的符号程序，以推荐下一步操作。这种方法大大减少了数据注释和模型培训要求，从而解决了快速调整TOD系统以看不见的任务和域的持久挑战。我们在Star，ABCD和SGD基准测试中展示了最先进的结果。我们还在低资源设置（例如Multiwoz上的零射击）中展示了强零转移能力。此外，我们发布了STARV2，这是带有更丰富注释的Star数据集的更新版本，用于基准零射击端到端TOD模型。
We propose AnyTOD, an end-to-end, zero-shot task-oriented dialog (TOD) system capable of handling unseen tasks without task-specific training. We view TOD as a program executed by a language model (LM), where program logic and ontology is provided by a designer as a schema. To enable generalization to unseen schemas and programs without prior training, AnyTOD adopts a neuro-symbolic approach. A neural LM keeps track of events occurring during a conversation and a symbolic program implementing the dialog policy is executed to recommend next actions AnyTOD should take. This approach drastically reduces data annotation and model training requirements, addressing the enduring challenge of rapidly adapting a TOD system to unseen tasks and domains. We demonstrate state-of-the-art results on STAR, ABCD and SGD benchmarks. We also demonstrate strong zero-shot transfer ability in low-resource settings, such as zero-shot on MultiWOZ. In addition, we release STARv2, an updated version of the STAR dataset with richer annotations, for benchmarking zero-shot end-to-end TOD models.
