Abstract
The comprehensive descriptions for factual
attribute-value tables, which should be accurate, informative and loyal, can be very helpful
for end users to understand the structured data
in this form. However previous neural generators might suffer from key attributes missing, less informative and groundless information problems, which impede the generation of
high-quality comprehensive descriptions for
tables. To relieve these problems, we first propose force attention (FA) method to encourage the generator to pay more attention to the
uncovered attributes to avoid potential key attributes missing. Furthermore, we propose reinforcement learning for information richness
to generate more informative as well as more
loyal descriptions for tables. In our experiments, we utilize the widely used WIKIBIO
dataset as a benchmark. Additionally we create WB-filter based on WIKIBIO to test
our model in the simulated user-oriented scenarios, in which the generated descriptions
should accord with particular user interests.
Experimental results show that our model outperforms the state-of-the-art baselines on both
automatic and human evaluation