论文标题
对话框状态跟踪模型的外套外培训
Out-of-Task Training for Dialog State Tracking Models
论文作者
论文摘要
对话状态跟踪（DST）患有严重的数据稀疏性。尽管许多自然语言处理（NLP）任务受益于转移学习和多任务学习，但在对话框中，这些方法受到可用数据量和对话应用程序的特异性的限制。在这项工作中，我们成功地利用了来自无关的NLP任务中的非DIALOG数据来训练对话率状态跟踪器。这为不相关的NLP Corpora提供了大门，以减轻DST固有的数据稀疏问题。
Dialog state tracking (DST) suffers from severe data sparsity. While many natural language processing (NLP) tasks benefit from transfer learning and multi-task learning, in dialog these methods are limited by the amount of available data and by the specificity of dialog applications. In this work, we successfully utilize non-dialog data from unrelated NLP tasks to train dialog state trackers. This opens the door to the abundance of unrelated NLP corpora to mitigate the data sparsity issue inherent to DST.
