The Enhanced Arabchat: An Arabic Conversational Agent

The Enhanced ArabChat is a complement of the previous version of ArabChat. This paper details an enhancements development of a novel and practical Conversational Agent for the Arabic language called the “Enhanced ArabChat”. A conversational Agent is a computer program that attempts to simulate conversations between machine and human. Some of lessons was learned by evaluating the previous work of ArabChat . These lessons revealed that two major issues affected the ArabChat’s performance negatively. Firstly, the need for a technique to distinguish between question and non-question utterances to reply with a more suitable response depending on the utterance’s type (question and non-question based utterances). Secondly, the need for a technique to handle an utterance targeting many topics that require firing many rules at the same time. Therefore, in this paper, the “Enhanced ArabChat” will cover these enhancements to improve the ArabChat’s performance. A real experiment has been done in Applied Science University in Jordan as an information point advisor for their native Arabic students to evaluate the Enhanced ArabChat

A General Evaluation Framework for Text Based Conversational Agent

This paper details the development of a new evaluation framework for a text based Conversational Agent (CA). A CA is an intelligent system that handle spoken or/and text based conversations between machine and human. Generally, the lack of evaluation frameworks for CAs effects its development. The idea behind any system’s evaluation is to make sure about the system’s functionalities and to continue development on it. A specific CA has been chosen to test the proposed framework on it; namely ArabChat. The ArabChat is a rule based CA and uses pattern matching technique to handle user’s Arabic text based conversations. The proposed and developed evaluation framework in this paper is natural language independent. The proposed framework is based on the exchange of specific information between ArabChat and user called “Information Requirements”. This information are tagged for each rule in the applied domain and should be exist in a user’s utterance (conversation). A real experiment has been done in Applied Science University in Jordan as an information point advisor for their native Arabic students to evaluate the ArabChat and then evaluating the proposed evaluation framework

Towards a New Generation of Conversational Agents Based on Sentence Similarity.

The Conversational Agent (CA) is a computer program that can engage in conversation using natural language dialogue with a human participant. Most CAs employ a pattern-matching technique to map user input onto structural patterns of sentences. However, every combination of utterances that a user may send as input must be taken into account when constructing such a script. This chapter was concerned with constructing a novel CA using sentence similarity measures. Examining word meaning rather than structural patterns of sentences meant that scripting was reduced to a couple of natural language sentences per rule as opposed to potentially 100s of patterns. Furthermore, initial results indicate good sentence similarity matching with 13 out of 18 domain-specific user utterances as opposed to that of the traditional pattern matching approach

Measuring Human Comprehension from Nonverbal Behaviour using Artificial Neural Networks

This paper presents the adaptation and application of Silent Talker, a psychological profiling system in the measurement of human comprehension through the monitoring of multiple channels of facial nonverbal behaviour using Artificial Neural Networks (ANN). Everyday human interactions are abundant with almost unconscious nonverbal behaviours accounting for approximately 93% of communication, providing a potentially rich source of information once decoded. Existing comprehension assessments techniques are inhibited by inconsistencies, limited to the verbal communication dimension and are often time-consuming with feedback delay. Major weaknesses hinder humans as accurate decoders of nonverbal behaviour with being error prone, inconsistent and poor at simultaneously focusing on multiple channels. Furthermore, human decoders are susceptible to fatigue and require training resulting in a costly, time-consuming process. ANNs are powerful, adaptable, scalable computational models that are able to overcome human decoder and pattern classification weaknesses. Therefore, the neural networks computer-based Silent Talker system has been trained and validated in the measurement of human comprehension using videotaped participant nonverbal behaviour from an informed consent field study. A series of experiments on training backpropagation ANNs with different topologies were conducted. The results show that comprehension and non comprehension patterns exist within the monitored multichannels of facial NVB with both experiments consistently yielding classification accuracies above 80%

FATHOM: A Neural Network-based Non-verbal Human Comprehension Detection System for Learning Environments

This paper presents the application of FATHOM, a computerised non-verbal comprehension detection system, to distinguish participant comprehension levels in an interactive tutorial. FATHOM detects high and low levels of human comprehension by concurrently tracking multiple non-verbal behaviours using artificial neural networks. Presently, human comprehension is predominantly monitored from written and spoken language. Therefore, a large niche exists for exploring human comprehension detection from a non-verbal behavioral perspective using artificially intelligent computational models such as neural networks. In this paper, FATHOM was applied to a video-recorded exploratory study containing a learning task designed to elicit high and low comprehension states from the learner. The learning task comprised of watching a video on termites, suitable for the general public and an interview led question and answer session. This paper describes how FATHOM’s comprehension classifier artificial neural network was trained and validated in comprehension detection using the standard backpropagation algorithm. The results show that high and low comprehension states can be detected from learner’s non-verbal behavioural cues with testing classification accuracies above 76%

On Predicting Learning Styles in Conversational Intelligent Tutoring Systems using Fuzzy Classification Trees

Oscar is a conversational intelligent tutoring system (CITS) which dynamically predicts and adapts to a student's learning style throughout the tutoring conversation. Oscar aims to mimic a human tutor to improve the effectiveness of the learning experience by leading a natural language tutorial and adapting material to suit an individual's learning style. Prediction of learning style is undertaken through capturing independent variables during the conversation. The variable with the highest value determines the individuals learning style. This paper proposes a new method which uses a fuzzy classification tree to build a fuzzy predictive model using these variables which are captured through natural language dialogue Experiments have been undertaken on two of the learning style dimensions: perception (sensory-intuitive) and understanding (sequential-global). Early results show the model has substantially increased the predictive accuracy of the Oscar CITS and discovered some interesting relationships amongst these variables