Paper Reading - Sequence to Sequence Learning with Neural Networks

Link of the Paper: https://arxiv.org/pdf/1409.3215.pdf

Main Points:

1. Encoder-Decoder Model: Input sequence -> A vector of a fixed dimensionality -> Target sequence.
2. A multilayered  LSTM: The LSTM did not have difficulty on long sentences.
3. Reverse Input: Better performance.

Other Key Points:

1. A significant limitation: Despite their flexibility and power, DNNs can only be applied to problems whose inputs and targets can be sensibly encoded with vectors of fixed dimensionality.