no code implementations • 1 Jul 2021 • Abdalkarim Mohtasib, Amir Ghalamzan E., Nicola Bellotto, Heriberto Cuayáhuitl
We compared the performances of our novel classifier and the existing models using our dataset and the MIME dataset.
no code implementations • 1 Jan 2021 • Siddique Latif, Heriberto Cuayáhuitl, Farrukh Pervez, Fahad Shamshad, Hafiz Shehbaz Ali, Erik Cambria
We begin with an introduction to the general field of DL and reinforcement learning (RL), then progress to the main DRL methods and their applications in the audio domain.
no code implementations • 27 Aug 2019 • Heriberto Cuayáhuitl, Donghyeon Lee, Seonghan Ryu, Yongjin Cho, Sungja Choi, Satish Indurthi, Seunghak Yu, Hyungtak Choi, Inchul Hwang, Jihie Kim
Experimental results using chitchat data reveal that (1) near human-like dialogue policies can be induced, (2) generalisation to unseen data is a difficult problem, and (3) training an ensemble of chatbot agents is essential for improved performance over using a single agent.
no code implementations • 27 Aug 2019 • Heriberto Cuayáhuitl, Donghyeon Lee, Seonghan Ryu, Sungja Choi, Inchul Hwang, Jihie Kim
Training chatbots using the reinforcement learning paradigm is challenging due to high-dimensional states, infinite action spaces and the difficulty in specifying the reward function.
no code implementations • 27 Aug 2019 • 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 autonomously.
no code implementations • 2 Dec 2018 • Heriberto Cuayáhuitl, Seonghan Ryu, Donghyeon Lee, Jihie Kim
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.
1 code implementation • 26 Nov 2016 • 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 small sets of example interactions.
1 code implementation • 26 Nov 2016 • Heriberto Cuayáhuitl, Seunghak Yu, Ashley Williamson, Jacob Carse
Standard deep reinforcement learning methods such as Deep Q-Networks (DQN) for multiple tasks (domains) face scalability problems.
1 code implementation • 18 Jan 2016 • Heriberto Cuayáhuitl
This paper presents 'SimpleDS', a simple and publicly available dialogue system trained with deep reinforcement learning.
1 code implementation • 25 Nov 2015 • Heriberto Cuayáhuitl, Simon Keizer, Oliver Lemon
This paper describes a successful application of Deep Reinforcement Learning (DRL) for training intelligent agents with strategic conversational skills, in a situated dialogue setting.