论文标题
零射击转移学习与合成数据多域对话状态跟踪
Zero-Shot Transfer Learning with Synthesized Data for Multi-Domain Dialogue State Tracking
论文作者
论文摘要
多域对话状态跟踪的零拍传输学习可以使我们能够处理新的域而不会产生高数据获取成本。本文提出了针对对话状态跟踪的新的零短传输学习技术，其中域内培训数据都是从抽象的对话模型和域的本体论中综合的。我们表明，通过合成数据扩大数据可以提高贸易模型的零摄像学习的准确性和MultiWoz 2.1数据集中的基于BERT的SUMBT模型。我们显示仅在SUMBT模型上合成的域内数据的训练可以达到使用完整培训数据集获得的准确性的2/3。我们平均使整个领域的零射门学习状态提高了21％。
Zero-shot transfer learning for multi-domain dialogue state tracking can allow us to handle new domains without incurring the high cost of data acquisition. This paper proposes new zero-short transfer learning technique for dialogue state tracking where the in-domain training data are all synthesized from an abstract dialogue model and the ontology of the domain. We show that data augmentation through synthesized data can improve the accuracy of zero-shot learning for both the TRADE model and the BERT-based SUMBT model on the MultiWOZ 2.1 dataset. We show training with only synthesized in-domain data on the SUMBT model can reach about 2/3 of the accuracy obtained with the full training dataset. We improve the zero-shot learning state of the art on average across domains by 21%.
