Neural approaches to conversational AI
This tutorial surveys neural approaches to conversational AI that were developed in the last
few years. We group conversational systems into three categories:(1) question answering …
few years. We group conversational systems into three categories:(1) question answering …
Pomdp-based statistical spoken dialog systems: A review
Statistical dialog systems (SDSs) are motivated by the need for a data-driven framework that
reduces the cost of laboriously handcrafting complex dialog managers and that provides …
reduces the cost of laboriously handcrafting complex dialog managers and that provides …
A neural network approach to context-sensitive generation of conversational responses
We present a novel response generation system that can be trained end to end on large
quantities of unstructured Twitter conversations. A neural network architecture is used to …
quantities of unstructured Twitter conversations. A neural network architecture is used to …
A survey of available corpora for building data-driven dialogue systems
During the past decade, several areas of speech and language understanding have
witnessed substantial breakthroughs from the use of data-driven models. In the area of …
witnessed substantial breakthroughs from the use of data-driven models. In the area of …
A deep reinforcement learning chatbot
We present MILABOT: a deep reinforcement learning chatbot developed by the Montreal
Institute for Learning Algorithms (MILA) for the Amazon Alexa Prize competition. MILABOT is …
Institute for Learning Algorithms (MILA) for the Amazon Alexa Prize competition. MILABOT is …
Chai: A chatbot ai for task-oriented dialogue with offline reinforcement learning
Conventionally, generation of natural language for dialogue agents may be viewed as a
statistical learning problem: determine the patterns in human-provided data and generate …
statistical learning problem: determine the patterns in human-provided data and generate …
Continual learning for instruction following from realtime feedback
We propose and deploy an approach to continually train an instruction-following agent from
feedback provided by users during collaborative interactions. During interaction, human …
feedback provided by users during collaborative interactions. During interaction, human …
A sequence-to-sequence model for user simulation in spoken dialogue systems
User simulation is essential for generating enough data to train a statistical spoken dialogue
system. Previous models for user simulation suffer from several drawbacks, such as the …
system. Previous models for user simulation suffer from several drawbacks, such as the …
Report from the nsf future directions workshop on automatic evaluation of dialog: Research directions and challenges
This is a report on the NSF Future Directions Workshop on Automatic Evaluation of Dialog.
The workshop explored the current state of the art along with its limitations and suggested …
The workshop explored the current state of the art along with its limitations and suggested …
Improving dialog systems for negotiation with personality modeling
In this paper, we explore the ability to model and infer personality types of opponents,
predict their responses, and use this information to adapt a dialog agent's high-level strategy …
predict their responses, and use this information to adapt a dialog agent's high-level strategy …