@inproceedings{farag-etal-2017-error,
title = "An Error-Oriented Approach to Word Embedding Pre-Training",
author = "Farag, Youmna and
Rei, Marek and
Briscoe, Ted",
editor = "Tetreault, Joel and
Burstein, Jill and
Leacock, Claudia and
Yannakoudakis, Helen",
booktitle = "Proceedings of the 12th Workshop on Innovative Use of {NLP} for Building Educational Applications",
month = sep,
year = "2017",
address = "Copenhagen, Denmark",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W17-5016",
doi = "10.18653/v1/W17-5016",
pages = "149--158",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T An Error-Oriented Approach to Word Embedding Pre-Training
%A Farag, Youmna
%A Rei, Marek
%A Briscoe, Ted
%Y Tetreault, Joel
%Y Burstein, Jill
%Y Leacock, Claudia
%Y Yannakoudakis, Helen
%S Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F farag-etal-2017-error
%X 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.
%R 10.18653/v1/W17-5016
%U https://aclanthology.org/W17-5016
%U https://doi.org/10.18653/v1/W17-5016
%P 149-158
Markdown (Informal)
[An Error-Oriented Approach to Word Embedding Pre-Training](https://aclanthology.org/W17-5016) (Farag et al., BEA 2017)
ACL
- Youmna Farag, Marek Rei, and Ted Briscoe. 2017. An Error-Oriented Approach to Word Embedding Pre-Training. In Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications, pages 149–158, Copenhagen, Denmark. Association for Computational Linguistics.