1 code implementation • INLG (ACL) 2020 • Symon Stevens-Guille, Aleksandre Maskharashvili, Amy Isard, Xintong Li, Michael White
While classic NLG systems typically made use of hierarchically structured content plans that included discourse relations as central components, more recent neural approaches have mostly mapped simple, flat inputs to texts without representing discourse relations explicitly.
1 code implementation • INLG (ACL) 2021 • Aleksandre Maskharashvili, Symon Stevens-Guille, Xintong Li, Michael White
Recent developments in natural language generation (NLG) have bolstered arguments in favor of re-introducing explicit coding of discourse relations in the input to neural models.
2 code implementations • INLG (ACL) 2021 • Xintong Li, Symon Stevens-Guille, Aleksandre Maskharashvili, Michael White
Neural approaches to natural language generation in task-oriented dialogue have typically required large amounts of annotated training data to achieve satisfactory performance, especially when generating from compositional inputs.
1 code implementation • SIGDIAL (ACL) 2022 • Symon Stevens-Guille, Aleksandre Maskharashvili, Xintong Li, Michael White
Our results suggest that including discourse relation information in the input of the model significantly improves the consistency with which it produces a correctly realized discourse relation in the output.
no code implementations • 22 Sep 2020 • Richard Moot, Symon Stevens-Guille
This paper explores proof-theoretic aspects of hybrid type-logical grammars , a logic combining Lambek grammars with lambda grammars.
no code implementations • WS 2017 • Taylor Mahler, Willy Cheung, Micha Elsner, David King, Marie-Catherine de Marneffe, Cory Shain, Symon Stevens-Guille, Michael White
This paper describes our {``}breaker{''} submission to the 2017 EMNLP {``}Build It Break It{''} shared task on sentiment analysis.