Constrained Decoding for Neural NLG from Compositional Representations in Task-Oriented Dialogue
Year: 2019
Type: conference-paper
Abstract: Generating fluent natural language responses from structured semantic representations is a critical step in task-oriented conversational systems.Avenues like the E2E NLG Challenge have encouraged the development of neural approaches, particularly sequence-tosequence (Seq2Seq) models for this problem.The semantic representations used, however, are often underspecified, which places a higher burden on the generation model for sentence planning, and also limits the extent to which generated responses can be controlled in a live system.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.
DOI: 10.18653/v1/p19-1080
Institutions: Meta (United States), The Ohio State University
Language: English
Topic: Speech and dialogue systems
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W2949413855