GEC Convolutional Encoder-Decoder Neural Network

A GEC Convolutional Encoder-Decoder Neural Network is a multilayer convolutional encoder-decoder neural network that is trained to solve a grammatical error correction.

• Example(s):
  • …
• Counter-Example(s):
  • a SegNet Convolutional Encoder-Decoder Neural Network,
  • a Recurrent Encoder-Decoder Neural Network,
  • a Transformer Neural Network.
• See: Sequence-to-Sequence Learning Task, Artificial Neural Network, Bidirectional Neural Network, Convolutional Neural Network, Neural Machine Translation Task, Deep Learning, Natural Language Processing.

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References

2018

• (Chollampatt & Ng, 2018) ⇒ Shamil Chollampatt, and Hwee Tou Ng. (2018). “A Multilayer Convolutional Encoder-Decoder Neural Network for Grammatical Error Correction.” In: Proceedings of the Thirty-Second Conference on Artificial Intelligence (AAAI-2018).
  • QUOTE: We improve automatic correction of grammatical, orthographic, and collocation errors in text using a multilayer convolutional encoder-decoder neural network. The network is initialized with embeddings that make use of character N-gram information to better suit this task.

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    The encoder and decoder are made up of [math]\displaystyle{ L }[/math] layers each. The architecture of the network is shown in Figure 1. The source token embeddings, [math]\displaystyle{ s_1, \cdots, s_m }[/math], are linearly mapped to get input vectors of the first encoder layer, [math]\displaystyle{ h^0_1 , \cdots, h^0_m, }[/math] where [math]\displaystyle{ h^0_i \in R^h }[/math] and [math]\displaystyle{ h }[/math] is the input and output dimension of all encoder and decoder layers.

Figure 1: Architecture of our multilayer convolutional model with seven encoder and seven decoder layers (only one encoder and one decoder layer are illustrated in detail).

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