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Temporal Hierarchies in Sequence to Sequence for Sentence Correction

Abstract: "This work tackles sentence correction in the language domain by approaching it as a sequence to sequence (seq2seq) problem with the help of temporal hierarchies. It does so by implementing a Multiple Timescales model of the Gated Recurrent Unit (MTGRU) in a Recurrent Neural Network (RNN) Encoder-Decoder framework, which can perform more meaningful data abstraction even in the presence of errors. The proposed language correction model is compared to three baseline models: conventional RNN, Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU); by using a newly built dataset that consists of incorrect and correct sentences as input and target respectively. The result shows that the MTGRU model has a better generalization performance and outperforms all three models on the BLEU-$n$ evaluation metric."

Cite as:

@inproceedings{sergio2018temporal,
  title={Temporal Hierarchies in Sequence to Sequence for Sentence Correction},
  author={Sergio, Gwenaelle Cunha and Moirangthem, Dennis Singh and Lee, Minho},
  booktitle={2018 International Joint Conference on Neural Networks (IJCNN)},
  pages={1--7},
  year={2018},
  organization={IEEE}
}