Learning the Chinese Sentence Representation with LSTM Autoencoder
Mu‐Yen Chen, Tien‐Chi Huang, Yu Shu, Chia‐Chen Chen, Tsung-Che Hsieh, Neil Yuwen Yen · 2018
This study retains the meanings of the original text using Autoencoder (AE) in this regard. This study uses the different loss (includes three types) to train the neural network model, hopes that after compressing sentence features, it can still decompress the original input sentences and classify the correct targets, such as positive or negative sentiment. In this way, it supposed to get the more relative features (compressing sentence features) in the sentences to classify the targets, rather than using the classification loss that may classify by the meaningless features (words). In the result, this study discovers that adding additional features for correction of errors does not interfere with the learning. Also, not all words are needed to be restored without distortion after applying the AE method.