Abstract
We observe that end-to-end memory networks (MN) trained for task-oriented dialogue, such as for recommending restaurants to a user, suffer from an out-of-vocabulary (OOV) problem -- the entities returned by the Knowledge Base (KB) may not be seen by the network at training time, making it impossible for it to use them in dialogue. We propose a Hierarchical Pointer Memory Network (HyP-MN), in which the next word may be generated from the decode vocabulary or copied from a hierarchical memory maintaining KB results and previous utterances. Evaluating over the dialog bAbI tasks, we find that HyP-MN drastically outperforms MN obtaining 12% overall accuracy gains. Further analysis reveals that MN fails completely in recommending any relevant restaurant, whereas HyP-MN recommends the best next restaurant 80% of the time.
Abstract (translated)
我们观察到,针对面向任务的对话(例如向用户推荐餐馆)训练的端对端存储器网络(MN)受到词汇外(OOV)问题的困扰 - 由知识库返回的实体(KB)在培训期间可能不会被网络看到,使其无法在对话中使用它们。我们提出了一种分层指针存储器网络(HyP-MN),其中下一个字可以从解码词汇表生成,或者从维护KB结果和先前话语的分层存储器中复制。通过评估对话bAbI任务,我们发现HyP-MN明显优于MN,获得12%的整体准确性收益。进一步分析表明,MN未能完全推荐任何相关的餐厅,而HyP-MN建议80%的时间里最好的下一家餐厅。
URL
https://arxiv.org/abs/1805.01216