Attention-Informed Mixed-Language Training for Zero-shot Cross-lingual Task-oriented Dialogue Systems

@article{Liu2020AttentionInformedMT,
  title={Attention-Informed Mixed-Language Training for Zero-shot Cross-lingual Task-oriented Dialogue Systems},
  author={Zihan Liu and Genta Indra Winata and Zhaojiang Lin and Peng Xu and Pascale Fung},
  journal={ArXiv},
  year={2020},
  volume={abs/1911.09273}
}
Recently, data-driven task-oriented dialogue systems have achieved promising performance in English. However, developing dialogue systems that support low-resource languages remains a long-standing challenge due to the absence of high-quality data. In order to circumvent the expensive and time-consuming data collection, we introduce Attention-Informed Mixed-Language Training (MLT), a novel zero-shot adaptation method for cross-lingual task-oriented dialogue systems. It leverages very few task… Expand
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