STUDY OF DECONVOLUTIONAL SENTENCE RECONSTRUCTION
Finding an effective latent sentence representation is often a required step in most of the natural language processing (NLP) research. Most of the time, people are using recurrent neural networks to extract this latent representation with additional techniques like attention mechanism and teacher forcing. However, such techniques might weaken the amount of information carried inside the latent representation because the decoding process is not relying on the latent representation solely, but more on some external signals. In this essay, we are going to explore how we make use of deconvolution networks for sentence reconstruction. This can be potentially extended to solve other NLP tasks such as machine translation and sentiment analysis. From our sentence reconstruction experiment, the deconvolutional method outperforms a standard RNN method on short sentences.