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An Error-Oriented Approach to Word Embedding Pre-Training

Published version
Peer-reviewed

Type

Conference Object

Change log

Authors

Farag, Y 
Rei, M 
Briscoe, T 

Abstract

We propose a novel word embedding pre-training approach that exploits writing errors in learners' scripts. We compare our method to previous models that tune the embeddings based on script scores and the discrimination between correct and corrupt word contexts in addition to the generic commonly-used embeddings pre-trained on large corpora. The comparison is achieved by using the aforementioned models to bootstrap a neural network that learns to predict a holistic score for scripts. Furthermore, we investigate augmenting our model with error corrections and monitor the impact on performance. Our results show that our error-oriented approach outperforms other comparable ones which is further demonstrated when training on more data. Additionally, extending the model with corrections provides further performance gains when data sparsity is an issue.

Description

Keywords

cs.CL, cs.CL, cs.LG, cs.NE

Journal Title

Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications

Conference Name

12th Workshop on Innovative Use of NLP for Building Educational Applications

Journal ISSN

Volume Title

Publisher

Association for Computational Linguistics
Sponsorship
Cambridge Assessment (unknown)