Improving Conditioning in Context-Aware Sequence to Sequence Models
arxiv(2019)
摘要
Neural sequence to sequence models are well established for applications which can be cast as mapping a single input sequence into a single output sequence. In this work, we focus on cases where generation is conditioned on both a short query and a long context, such as abstractive question answering or document-level translation. We modify the standard sequence-to-sequence approach to make better use of both the query and the context by expanding the conditioning mechanism to intertwine query and context attention. We also introduce a simple and efficient data augmentation method for the proposed model. Experiments on three different tasks show that both changes lead to consistent improvements.
更多查看译文
关键词
sequence models,conditioning,context-aware
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络