Realistic grammar error simulation using Markov Logic

Authors:
Sungjin Lee;Gary Geunbae Lee
Affiliations:
Pohang University of Science and, Technology, Pohang, Korea;Pohang University of Science and, Technology, Pohang, Korea
Venue:
ACLShort '09 Proceedings of the ACL-IJCNLP 2009 Conference Short Papers
Year:
2009

Citing 4
Cited 1

Probabilistic reasoning in intelligent systems: networks of plausible inference

Probabilistic reasoning in intelligent systems: networks of plausible inference
Markov logic networks

Machine Learning
A survey of statistical user simulation techniques for reinforcement-learning of dialogue management strategies

The Knowledge Engineering Review
Treebanks gone bad: Parser evaluation and retraining using a treebank of ungrammatical sentences

International Journal on Document Analysis and Recognition

POMY: a conversational virtual environment for language learning in POSTECH

SIGDIAL '11 Proceedings of the SIGDIAL 2011 Conference

Quantified Score

Hi-index	0.00

Visualization

Abstract

The development of Dialog-Based Computer-Assisted Language Learning (DB-CALL) systems requires research on the simulation of language learners. This paper presents a new method for generation of grammar errors, an important part of the language learner simulator. Realistic errors are generated via Markov Logic, which provides an effective way to merge a statistical approach with expert knowledge about the grammar error characteristics of language learners. Results suggest that the distribution of simulated grammar errors generated by the proposed model is similar to that of real learners. Human judges also gave consistently close judgments on the quality of the real and simulated grammar errors.