18 papers with code • 0 benchmarks • 1 datasets
( Image credit: Bocklisch et al. )
These leaderboards are used to track progress in Dialogue Management
Rethinking Action Spaces for Reinforcement Learning in End-to-end Dialog Agents with Latent Variable Models
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.
Conversation Graph: Data Augmentation, Training and Evaluation for Non-Deterministic Dialogue Management
We propose the Conversation Graph (ConvGraph), a graph-based representation of dialogues that can be exploited for data augmentation, multi-reference training and evaluation of non-deterministic agents.
This paper describes a successful application of Deep Reinforcement Learning (DRL) for training intelligent agents with strategic conversational skills, in a situated dialogue setting.
We introduce a pair of tools, Rasa NLU and Rasa Core, which are open source python libraries for building conversational software.
Recent statistical approaches have improved the robustness and scalability of spoken dialogue systems.
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.
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.