The Generation of Textual Entailment with NLML in an Intelligent Dialogue system for Language Learning CSIEC

TL;DR

This paper presents a novel approach to generate textual entailment within an educational dialogue system for English learning, using rule annotation in NLML and pattern matching, aiming to improve language instruction effectiveness.

Contribution

It introduces a new method for textual entailment generation in an educational dialogue system using rule annotation and pattern recognition within the CSIEC framework.

Findings

Algorithm tested with various entailment examples.

Rule annotation approach tailored for English textbooks.

Future work includes GUI for rule editing.

Abstract

This research report introduces the generation of textual entailment within the project CSIEC (Computer Simulation in Educational Communication), an interactive web-based human-computer dialogue system with natural language for English instruction. The generation of textual entailment (GTE) is critical to the further improvement of CSIEC project. Up to now we have found few literatures related with GTE. Simulating the process that a human being learns English as a foreign language we explore our naive approach to tackle the GTE problem and its algorithm within the framework of CSIEC, i.e. rule annotation in NLML, pattern recognition (matching), and entailment transformation. The time and space complexity of our algorithm is tested with some entailment examples. Further works include the rules annotation based on the English textbooks and a GUI interface for normal users to edit the entailment rules.