  This study presents a novel model for invertible sentence embeddings using a
residual recurrent network trained on an unsupervised encoding task. Rather
than the probabilistic outputs common to neural machine translation models, our
approach employs a regression-based output layer to reconstruct the input
sequence's word vectors. The model achieves high accuracy and fast training
with the ADAM optimizer, a significant finding given that RNNs typically
require memory units, such as LSTMs, or second-order optimization methods. We
incorporate residual connections and introduce a "match drop" technique, where
gradients are calculated only for incorrect words. Our approach demonstrates
potential for various natural language processing applications, particularly in
neural network-based systems that require high-quality sentence embeddings.
