Towards Comprehensive Description Generation from Factual Attribute-value Tables Tianyu Liu1, Fuli Luo1, Pengcheng Yang1, Wei Wu1, Baobao Chang1,2 and Zhifang Sui1,2 1MOE Key Lab of Computational Linguistics, School of EECS, Peking University 2Peng Cheng Laboratory, Shenzhen, China ftianyu0421, luofuli, yang pc, wu.wei, chbb,
[email protected] Abstract Attribute Value Birthplace Utah, America Position forward (soccer player) The comprehensive descriptions for factual attribute-value tables, which should be accu- Comprehensive:A Utah soccer player who plays as forward rate, informative and loyal, can be very helpful Missing Key Attri.:A soccer player who plays as forward Groundless info:A Utah forward in the national team for end users to understand the structured data Less Informative: An American forward in this form. However previous neural gen- erators might suffer from key attributes miss- Table 1: An example for comprehensive generation. ing, less informative and groundless informa- Suppose we only have two attribute-value tuples, the tion problems, which impede the generation of underlined content is groundless information not men- high-quality comprehensive descriptions for tioned in source tables. tables. To relieve these problems, we first pro- pose force attention (FA) method to encour- age the generator to pay more attention to the tive and groundless content in its generated de- uncovered attributes to avoid potential key at- tributes missing. Furthermore, we propose re- scriptions towards source tables. For example, in inforcement learning for information richness Table1, the ‘missing key attribute’ case doesn’t to generate more informative as well as more mention where the player comes from (birthplace) loyal descriptions for tables.