An Encoder with non-Sequential Dependency for Neural Data-to-Text Generation

Feng Nie, Jinpeng Wang, Rong Pan, Chin-Yew Lin
2019 Proceedings of the 12th International Conference on Natural Language Generation  
Data-to-text generation aims to generate descriptions given a structured input data (i.e., a table with multiple records). Existing neural methods for encoding input data can be divided into two categories: a) pooling based encoders which ignore dependencies between input records or b) recurrent encoders which model only sequential dependencies between input records. In our investigation, although the recurrent encoder generally outperforms the pooling based encoder by learning the sequential
more » ... ng the sequential dependencies, it is sensitive to the order of the input records (i.e., performance decreases when injecting the random shuffling noise over input data). To overcome this problem, we propose to adopt the self-attention mechanism to learn dependencies between arbitrary input records. Experimental results show the proposed method achieves comparable results and remains stable under random shuffling over input data.
doi:10.18653/v1/w19-8619 dblp:conf/inlg/NieWPL19 fatcat:usypfzzrkzgmforvblune5brb4