CASIA OpenIR  > 自然语言处理团队
Sequence Generation: From Both Sides to the Middle
Long Zhou; Jiajun Zhang; Chengqing Zong; Heng Yu
2019
Conference NameIJCAI-2019
Conference Date2019
Conference PlaceMacau, China
Abstract

The encoder-decoder framework has achieved promising process for many sequence generation tasks, such as neural machine translation and text summarization. Such a framework usually generates a sequence token by token from left to right, hence (1) this autoregressive decoding procedure is time-consuming when the output sentence becomes longer, and (2) it lacks the guidance of future context which is crucial to avoid under-translation. To alleviate these issues, we propose a synchronous bidirectional sequence generation (SBSG) model which predicts its outputs from both sides to the middle simultaneously. In the SBSG model, we enable the left-to-right (L2R) and right-to-left (R2L) generation to help and interact with each other by leveraging interactive bidirectional attention network. Experiments on neural machine translation (En⇒De, Ch⇒En, and En⇒Ro) and text summarization tasks show that the proposed model significantly speeds up decoding while improving the generation quality compared to the autoregressive Transformer.

Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/26134
Collection自然语言处理团队
Affiliation中国科学院自动化研究所
Recommended Citation
GB/T 7714
Long Zhou,Jiajun Zhang,Chengqing Zong,et al. Sequence Generation: From Both Sides to the Middle[C],2019.
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