85 citations · 231 across the 11 of their papers we have counts for
12 papers
A Dataset of Robot-Patient and Doctor-Patient Medical Dialogues for Spoken Language Processing Tasks
Heriberto Cuayahuitl, Grace Jang
Large Language Models (LLMs) have brought huge improvements to Artificial Intelligence (AI), which can be applied to general-purpose tasks. However, their application to textual or…
Transformers in Speech Processing: A Survey
Siddique Latif, Aun Zaidi, Heriberto Cuayahuitl +4
The remarkable success of transformers in the field of natural language processing has sparked the interest of the speech-processing community, leading to an exploration of their p…
Robot Policy Learning from Demonstration Using Advantage Weighting and Early Termination
Abdalkarim Mohtasib, Gerhard Neumann, Heriberto Cuayahuitl
Learning robotic tasks in the real world is still highly challenging and effective practical solutions remain to be found. Traditional methods used in this area are imitation learn…
A Study on Dense and Sparse (Visual) Rewards in Robot Policy Learning
Abdalkarim Mohtasib, Gerhard Neumann, Heriberto Cuayahuitl
Deep Reinforcement Learning (DRL) is a promising approach for teaching robots new behaviour. However, one of its main limitations is the need for carefully hand-coded reward signal…
Ensemble-Based Deep Reinforcement Learning for Chatbots
Heriberto Cuayáhuitl, Donghyeon Lee, Seonghan Ryu +7
Trainable chatbots that exhibit fluent and human-like conversations remain a big challenge in artificial intelligence. Deep Reinforcement Learning (DRL) is promising for addressing…
A Data-Efficient Deep Learning Approach for Deployable Multimodal Social Robots
Heriberto Cuayáhuitl
The deep supervised and reinforcement learning paradigms (among others) have the potential to endow interactive multimodal social robots with the ability of acquiring skills autono…
Deep Reinforcement Learning for Chatbots Using Clustered Actions and Human-Likeness Rewards
Heriberto Cuayáhuitl, Donghyeon Lee, Seonghan Ryu +3
Training chatbots using the reinforcement learning paradigm is challenging due to high-dimensional states, infinite action spaces and the difficulty in specifying the reward functi…
A Study on Dialogue Reward Prediction for Open-Ended Conversational Agents
Heriberto Cuayáhuitl, Seonghan Ryu, Donghyeon Lee +1
The amount of dialogue history to include in a conversational agent is often underestimated and/or set in an empirical and thus possibly naive way. This suggests that principled in…
Deep Reinforcement Learning for Multi-Domain Dialogue Systems
Heriberto Cuayáhuitl, Seunghak Yu, Ashley Williamson +1
Standard deep reinforcement learning methods such as Deep Q-Networks (DQN) for multiple tasks (domains) face scalability problems. We propose a method for multi-domain dialogue pol…
Training an Interactive Humanoid Robot Using Multimodal Deep Reinforcement Learning
Heriberto Cuayáhuitl, Guillaume Couly, Clément Olalainty
Training robots to perceive, act and communicate using multiple modalities still represents a challenging problem, particularly if robots are expected to learn efficiently from sma…
SimpleDS: A Simple Deep Reinforcement Learning Dialogue System
Heriberto Cuayáhuitl
This paper presents 'SimpleDS', a simple and publicly available dialogue system trained with deep reinforcement learning. In contrast to previous reinforcement learning dialogue sy…
Strategic Dialogue Management via Deep Reinforcement Learning
Heriberto Cuayáhuitl, Simon Keizer, Oliver Lemon
Artificially intelligent agents equipped with strategic skills that can negotiate during their interactions with other natural or artificial agents are still underdeveloped. This p…