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Recent advances in deep learning based dialogue systems: A systematic survey
Dialogue systems are a popular natural language processing (NLP) task as it is promising in
real-life applications. It is also a complicated task since many NLP tasks deserving study are …
real-life applications. It is also a complicated task since many NLP tasks deserving study are …
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 …
Human-level play in the game of Diplomacy by combining language models with strategic reasoning
Meta Fundamental AI Research Diplomacy Team … - Science, 2022 - science.org
Despite much progress in training artificial intelligence (AI) systems to imitate human
language, building agents that use language to communicate intentionally with humans in …
language, building agents that use language to communicate intentionally with humans in …
Transferable multi-domain state generator for task-oriented dialogue systems
Over-dependence on domain ontology and lack of knowledge sharing across domains are
two practical and yet less studied problems of dialogue state tracking. Existing approaches …
two practical and yet less studied problems of dialogue state tracking. Existing approaches …
Opendialkg: Explainable conversational reasoning with attention-based walks over knowledge graphs
We study a conversational reasoning model that strategically traverses through a large-
scale common fact knowledge graph (KG) to introduce engaging and contextually diverse …
scale common fact knowledge graph (KG) to introduce engaging and contextually diverse …
A survey on dialogue systems: Recent advances and new frontiers
Dialogue systems have attracted more and more attention. Recent advances on dialogue
systems are overwhelmingly contributed by deep learning techniques, which have been …
systems are overwhelmingly contributed by deep learning techniques, which have been …
Deep reinforcement learning: An overview
Y Li - arxiv preprint arxiv:1701.07274, 2017 - arxiv.org
We give an overview of recent exciting achievements of deep reinforcement learning (RL).
We discuss six core elements, six important mechanisms, and twelve applications. We start …
We discuss six core elements, six important mechanisms, and twelve applications. We start …
Deep reinforcement learning
SE Li - Reinforcement learning for sequential decision and …, 2023 - Springer
Similar to humans, RL agents use interactive learning to successfully obtain satisfactory
decision strategies. However, in many cases, it is desirable to learn directly from …
decision strategies. However, in many cases, it is desirable to learn directly from …
Rasa: Open source language understanding and dialogue management
T Bocklisch, J Faulkner, N Pawlowski… - arxiv preprint arxiv …, 2017 - arxiv.org
We introduce a pair of tools, Rasa NLU and Rasa Core, which are open source python
libraries for building conversational software. Their purpose is to make machine-learning …
libraries for building conversational software. Their purpose is to make machine-learning …
Recent advances and challenges in task-oriented dialog systems
Due to the significance and value in human-computer interaction and natural language
processing, task-oriented dialog systems are attracting more and more attention in both …
processing, task-oriented dialog systems are attracting more and more attention in both …