August 1, 2019
Constrained Decoding for Neural NLG from Compositional Representations in Task-Oriented Dialogue
Annual Meeting of the Association for Computational Linguistics (ACL)
In this paper, we (1) propose using tree-structured semantic representations, like those used in traditional rule-based NLG systems, for better discourse-level structuring and sentence-level planning; (2) introduce a challenging dataset using this representation for the weather domain; (3) introduce a constrained decoding approach for Seq2Seq models that leverages this representation to improve semantic correctness; and (4) demonstrate promising results on our dataset and the E2E dataset.
By: Anusha Balakrishnan, Jinfeng Rao, Kartikeya Upasani, Michael White, Rajen Subba
Natural Language Processing & Speech