Semantically Conditioned LSTM-based Natural Language Generation for Spoken Dialogue Systems

Understand

Natural language generation (NLG) is a critical component of spoken dialogue and it has a significant impact both on usability and perceived quality.

• Most NLG systems in common use employ rules and heuristics and tend to generate rigid and stylised responses without the natural variation of human language.
• They are also not easily scaled to systems covering multiple domains and languages.
• This paper presents a statistical language generator based on a semantically controlled Long Short-term Memory (LSTM) structure.