Self-Learning Architecture for Natural Language Generation
Hyungtak Choi, K. M. Siddarth, Haehun Yang, Heesik Jeon, In-Chul Hwang, Jihie Kim · 2018
In this paper, we propose a self-learning architecture for generating natural language templates for conversational assistants.Generating templates to cover all the combinations of slots in an intent is time consuming and labor-intensive.We examine three different models based on our proposed architecture -Rule-based model, Sequence-to-Sequence (Seq2Seq) model and Semantically Conditioned LSTM (SC-LSTM) model for the IoT domain -to reduce the human labor required for template generation.We demonstrate the feasibility of template generation for the IoT domain using our self-learning architecture.In both automatic and human evaluation, the self-learning architecture outperforms previous works trained with a fully human-labeled dataset.This is promising for commercial conversational assistant solutions.