Recent progress in sequence-to-sequence (seq2seq) text generation models has delivered both fluent and content rich outputs by explicitly conducting content selection and ordering (Gehrmann et al., 2018;Wiseman et al., 2018), which is a promising avenue for enabling end-to-end counter-argument construction (Le et al., 2018).
our prior work (Hua and Wang, 2018) leverages passages retrieved from Wikipedia to improve the quality of generated arguments, yet Wikipedia itself has the limitation of containing mostly facts.