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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 twodoi:10.18653/v1/w18-6543 dblp:conf/inlg/ShimorinaG18 fatcat:6k73vqzjxzdqrkxdptwcdepb5e