Graph Convolutional Network with Sequential Attention for Goal-Oriented Dialogue Systems

Regularized Neural User Model for Goal Oriented Spoken Dialogue Systems

User simulation is widely used to generate artificial dialogues in order to train statistical spoken dialogue systems and perform evaluations. This paper presents a neural network approach for user modeling that exploits an encoderdecoder bidirectional architecture with a regularization layer for each dialogue act. In order to minimize the impact of data sparsity, the dialogue act space is comp...

Goal-oriented sequential pattern for network banking churn analysis

Discovering sequential patterns is one of the most important task in data mining. In this paper we propose an efficient algorithm, called Goal-oriented sequential pattern. It can provide enterprises warning signs soon before they are losing valuable customers and give them reference for decision making. Experiments comparing Apriori showed that Goal-oriented is more efficient, and performs reas...

Goal-Oriented Multimedia Dialogue with Variable Initiative

A dialogue algorithm is described that executes Prolog-style rules in an attempt to achieve a goal. The algorithm selects paths in the proof in an attempt to achieve success and proves subgoals on the basis of internally available information where possible. Where \missing axioms" are discovered, the algorithm interacts with the user to solve subgoals, and it uses the received information from ...

A Goal Oriented Model Of Human Dialogue

Building Effective Goal-Oriented Dialogue Agents

Recent progress has seen an explosion in dialog systems, including voice-activated bots, text-based chatbots, and email-based bots for scheduling meetings. However real life interaction with these so-called intelligent agents consistently fall short of expectations. Consequently, this project deploys neural-based bots and rule-based bots across a wide variety of tasks in order to analyze where ...