We introduce a pair of tools, Rasa NLU and Rasa Core, which are open source python libraries for building conversational software.
This paper describes a successful application of Deep Reinforcement Learning (DRL) for training intelligent agents with strategic conversational skills, in a situated dialogue setting.
Defining action spaces for conversational agents and optimizing their decision-making process with reinforcement learning is an enduring challenge.
End-to-end design of dialogue systems has recently become a popular research topic thanks to powerful tools such as encoder-decoder architectures for sequence-to-sequence learning.
Dialogue management (DM) plays a key role in the quality of the interaction with the user in a task-oriented dialogue system.
Dialogue embeddings are learned by a LSTM at the middle of the network, and updated by the feeding of all turn embeddings.
Semantic parsing aims to transform natural language (NL) utterances into formal meaning representations (MRs), whereas an NL generator achieves the reverse: producing a NL description for some given MRs.