Learning to Tag OOV Tokens by Integrating Contextual Representation and Background Knowledge

摘要

Neural-based context-aware models for slot tagging have achieved state-of-the-art performance. However, the presence of OOV(out-of-vocab) words significantly degrades the performance of neural-based models, especially in a few-shot scenario. In this paper, we propose a novel knowledge-enhanced slot tagging model to integrate contextual representation of input text and the large-scale lexical background knowledge. Besides, we use multi-level graph attention to explicitly model lexical relations. The experiments show that our proposed knowledge integration mechanism achieves consistent improvements across settings with different sizes of training data on two public benchmark datasets.

会议
ACL 2020
何可清
硕士研究生

对话系统,摘要,预训练

严渊蒙
严渊蒙
硕士研究生

自然语言理解,预训练

徐蔚然
徐蔚然
副教授,硕士生导师,博士生导师

信息检索,模式识别,机器学习