Data-Driven Broad-Coverage Grammars for Opinionated Natural Language Generation (ONLG)
Tomer Cagan, Stefan Leo Frank, Reut Tsarfaty · 2017
Opinionated natural language generation (ONLG) is a new, challenging, NLG task in which we aim to automatically generate human-like, subjective, responses to opinionated articles online.We present a data-driven architecture for ONLG that generates subjective responses triggered by users' agendas, based on automatically acquired wide-coverage generative grammars.We compare three types of grammatical representations that we design for ONLG.The grammars interleave different layers of linguistic information, and are induced from a new, enriched dataset we developed.Our evaluation shows that generation with Relational-Realizational (Tsarfaty and Sima'an, 2008) inspired grammar gets better language model scores than lexicalized grammars à la Collins (2003), and that the latter gets better humanevaluation scores.We also show that conditioning the generation on topic models makes generated responses more relevant to the document content.