An Error-Oriented Approach to Word Embedding Pre-Training
This work addresses the challenge of enhancing word embeddings for educational or language processing applications, though it appears incremental as it builds on existing tuning methods.
The paper tackles the problem of improving word embedding pre-training by leveraging writing errors from learners' scripts, and results show that this error-oriented approach outperforms comparable methods, with further gains when augmented with error corrections, especially under data sparsity conditions.
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.