This demo paper presents Emora STDM (State Transition Dialogue Manager), a dialogue system development framework that provides novel workflows for rapid prototyping of chat-based dialogue managers as well as collaborative development of complex interactions.
Dialogue embeddings are learned by a LSTM at the middle of the network, and updated by the feeding of all turn embeddings.
Dialogue management (DM) plays a key role in the quality of the interaction with the user in a task-oriented dialogue system.
In this paper, we focus on identity fraud detection in loan applications and propose to solve this problem with a novel interactive dialogue system which consists of two modules.
Defining action spaces for conversational agents and optimizing their decision-making process with reinforcement learning is an enduring challenge.
Even though machine learning has become the major scene in dialogue research community, the real breakthrough has been blocked by the scale of data available. To address this fundamental obstacle, we introduce the Multi-Domain Wizard-of-Oz dataset (MultiWOZ), a fully-labeled collection of human-human written conversations spanning over multiple domains and topics. At a size of 10k dialogues, it is at least one order of magnitude larger than all previous annotated task-oriented corpora. The contribution of this work apart from the open-sourced dataset is two-fold:firstly, a detailed description of the data collection procedure along with a summary of data structure and analysis is provided.
We introduce an ontology-based dialogue manage(OntoDM), a dialogue manager that keeps the state of the conversation, provides a basis for anaphora resolution and drives the conversation via domain ontologies.
Recent statistical approaches have improved the robustness and scalability of spoken dialogue systems.
We introduce a pair of tools, Rasa NLU and Rasa Core, which are open source python libraries for building conversational software.