Natural Language Generation in Dialogue using Lexicalized and Delexicalized Data

Understand

Natural language generation plays a critical role in spoken dialogue systems.

• We present a new approach to natural language generation for task-oriented dialogue using recurrent neural networks in an encoder-decoder framework.
• In contrast to previous work, our model uses both lexicalized and delexicalized components i.e.
• slot-value pairs for dialogue acts, with slots and corresponding values aligned together.