Survey on reinforcement learning for language processing
V Uc-Cetina, N Navarro-Guerrero… - Artificial Intelligence …, 2023 - Springer
In recent years some researchers have explored the use of reinforcement learning (RL)
algorithms as key components in the solution of various natural language processing (NLP) …
algorithms as key components in the solution of various natural language processing (NLP) …
A survey on recent advances and challenges in reinforcement learning methods for task-oriented dialogue policy learning
Dialogue policy learning (DPL) is a key component in a task-oriented dialogue (TOD)
system. Its goal is to decide the next action of the dialogue system, given the dialogue state …
system. Its goal is to decide the next action of the dialogue system, given the dialogue state …
Multi-agent reinforcement learning: Methods, applications, visionary prospects, and challenges
Multi-agent reinforcement learning (MARL) is a widely used Artificial Intelligence (AI)
technique. However, current studies and applications need to address its scalability, non …
technique. However, current studies and applications need to address its scalability, non …
Efficient dialog policy learning by reasoning with contextual knowledge
Goal-oriented dialog policy learning algorithms aim to learn a dialog policy for selecting
language actions based on the current dialog state. Deep reinforcement learning methods …
language actions based on the current dialog state. Deep reinforcement learning methods …
Towards sentiment aided dialogue policy learning for multi-intent conversations using hierarchical reinforcement learning
Purpose Developing a Dialogue/Virtual Agent (VA) that can handle complex tasks (need) of
the user pertaining to multiple intents of a domain is challenging as it requires the agent to …
the user pertaining to multiple intents of a domain is challenging as it requires the agent to …
Learning dialog policies from weak demonstrations
Deep reinforcement learning is a promising approach to training a dialog manager, but
current methods struggle with the large state and action spaces of multi-domain dialog …
current methods struggle with the large state and action spaces of multi-domain dialog …
A dynamic goal adapted task oriented dialogue agent
Purpose Existing virtual agents (VAs) present in dialogue systems are either information
retrieval based or static goal-driven. However, in real-world situations, end-users might not …
retrieval based or static goal-driven. However, in real-world situations, end-users might not …
Efficient dialogue complementary policy learning via deep q-network policy and episodic memory policy
Deep reinforcement learning has shown great potential in training dialogue policies.
However, its favorable performance comes at the cost of many rounds of interaction. Most of …
However, its favorable performance comes at the cost of many rounds of interaction. Most of …
Augmenting knowledge through statistical, goal-oriented human-robot dialog
S Amiri, S Bajracharya, C Goktolgal… - 2019 IEEE/RSJ …, 2019 - ieeexplore.ieee.org
Some robots can interact with humans using natural language, and identify service requests
through human-robot dialog. However, few robots are able to improve their language …
through human-robot dialog. However, few robots are able to improve their language …
Dynamic reward-based dueling deep dyna-q: Robust policy learning in noisy environments
Task-oriented dialogue systems provide a convenient interface to help users complete tasks.
An important consideration for task-oriented dialogue systems is the ability to against the …
An important consideration for task-oriented dialogue systems is the ability to against the …