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Cross-lingual Transfer Learning for Multilingual Task Oriented Dialog
2019
Proceedings of the 2019 Conference of the North
One of the first steps in the utterance interpretation pipeline of many task-oriented conversational AI systems is to identify user intents and the corresponding slots. Since data collection for machine learning models for this task is time-consuming, it is desirable to make use of existing data in a high-resource language to train models in low-resource languages. However, development of such models has largely been hindered by the lack of multilingual training data. In this paper, we present
doi:10.18653/v1/n19-1380
dblp:conf/naacl/SchusterGSL19
fatcat:yjsz3ckdj5gwtdi32hbcqbanvq