43,790 research outputs found
Deep Active Learning for Dialogue Generation
We propose an online, end-to-end, neural generative conversational model for
open-domain dialogue. It is trained using a unique combination of offline
two-phase supervised learning and online human-in-the-loop active learning.
While most existing research proposes offline supervision or hand-crafted
reward functions for online reinforcement, we devise a novel interactive
learning mechanism based on hamming-diverse beam search for response generation
and one-character user-feedback at each step. Experiments show that our model
inherently promotes the generation of semantically relevant and interesting
responses, and can be used to train agents with customized personas, moods and
conversational styles.Comment: Accepted at 6th Joint Conference on Lexical and Computational
Semantics (*SEM) 2017 (Previously titled "Online Sequence-to-Sequence Active
Learning for Open-Domain Dialogue Generation" on ArXiv
The Virtual Tutor: Tasks for conversational agents in Online Collaborative Learning Environments
Online collaborative learning environments are becoming increasingly popular in higher education. E-tutors need to supervise, guide students and look out for conflicts within the online environment to ensure a successful learning experience. Web-based platforms allow for interactive elements such as conversational agents to disencumber the e-tutor. Repeatable tasks, which do not require a human response, can be automatized by these systems. The aim of this study is to identify and synthesize the tasks an e-tutor has and to investigate the automatisation potential with conversational agents. Using a design science research approach a literature review is conducted, identifying 13 tasks. Subsequently, a matrix is established, contrasting the tasks with requirements for the use of conversational agents. Furthermore, a virtual tutor framework is developed, clarifying the agent type selection, the technical structure and components for a prototype development in an online collaborative learning environment
A Contextualized Real-Time Multimodal Emotion Recognition for Conversational Agents using Graph Convolutional Networks in Reinforcement Learning
Owing to the recent developments in Generative Artificial Intelligence
(GenAI) and Large Language Models (LLM), conversational agents are becoming
increasingly popular and accepted. They provide a human touch by interacting in
ways familiar to us and by providing support as virtual companions. Therefore,
it is important to understand the user's emotions in order to respond
considerately. Compared to the standard problem of emotion recognition,
conversational agents face an additional constraint in that recognition must be
real-time. Studies on model architectures using audio, visual, and textual
modalities have mainly focused on emotion classification using full video
sequences that do not provide online features. In this work, we present a novel
paradigm for contextualized Emotion Recognition using Graph Convolutional
Network with Reinforcement Learning (conER-GRL). Conversations are partitioned
into smaller groups of utterances for effective extraction of contextual
information. The system uses Gated Recurrent Units (GRU) to extract multimodal
features from these groups of utterances. More importantly, Graph Convolutional
Networks (GCN) and Reinforcement Learning (RL) agents are cascade trained to
capture the complex dependencies of emotion features in interactive scenarios.
Comparing the results of the conER-GRL model with other state-of-the-art models
on the benchmark dataset IEMOCAP demonstrates the advantageous capabilities of
the conER-GRL architecture in recognizing emotions in real-time from multimodal
conversational signals.Comment: 5 pages (4 main + 1 reference), 2 figures. Submitted to IEEE FG202
Evorus: A Crowd-powered Conversational Assistant Built to Automate Itself Over Time
Crowd-powered conversational assistants have been shown to be more robust
than automated systems, but do so at the cost of higher response latency and
monetary costs. A promising direction is to combine the two approaches for high
quality, low latency, and low cost solutions. In this paper, we introduce
Evorus, a crowd-powered conversational assistant built to automate itself over
time by (i) allowing new chatbots to be easily integrated to automate more
scenarios, (ii) reusing prior crowd answers, and (iii) learning to
automatically approve response candidates. Our 5-month-long deployment with 80
participants and 281 conversations shows that Evorus can automate itself
without compromising conversation quality. Crowd-AI architectures have long
been proposed as a way to reduce cost and latency for crowd-powered systems;
Evorus demonstrates how automation can be introduced successfully in a deployed
system. Its architecture allows future researchers to make further innovation
on the underlying automated components in the context of a deployed open domain
dialog system.Comment: 10 pages. To appear in the Proceedings of the Conference on Human
Factors in Computing Systems 2018 (CHI'18
The use of animated agents in eālearning environments: an exploratory, interpretive case study
There is increasing interest in the use of animated agents in eālearning environments. However, empirical investigations of their use in online education are limited. Our aim is to provide an empirically based framework for the development and evaluation of animated agents in eālearning environments. Findings suggest a number of challenges, including the multiple dialogue models that animated agents will need to accommodate, the diverse range of roles that pedagogical animated agents can usefully support, the dichotomous relationship that emerges between these roles and that of the lecturer, and student perception of the degree of autonomy that can be afforded to animated agents
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