Handling Rare Items in Data-to-Text Generation

Anastasia Shimorina, Claire Gardent
2018 Proceedings of the 11th International Conference on Natural Language Generation  
Neural approaches to data-to-text generation generally handle rare input items using either delexicalisation or a copy mechanism. We investigate the relative impact of these two methods on two datasets (E2E and WebNLG) and using two evaluation settings. We show (i) that rare items strongly impact performance; (ii) that combining delexicalisation and copying yields the strongest improvement; (iii) that copying underperforms for rare and unseen items and (iv) that the impact of these two
more » ... these two mechanisms greatly varies depending on how the dataset is constructed and on how it is split into train, dev and test 1 .
doi:10.18653/v1/w18-6543 dblp:conf/inlg/ShimorinaG18 fatcat:6k73vqzjxzdqrkxdptwcdepb5e