The Encoder-Decoder Framework and Its Applications

The neural encoder-decoder framework has advanced the state-of-the-art in machine translation significantly. Many researchers in recent years have employed the encoder-decoder based models to solve sophisticated tasks such as image/video captioning, textual/visual question answering, and text summarization. In this work we study the baseline encoder-decoder framework in machine translation and take a brief look at the encoder structures proposed to cope with the difficulties of feature extraction. Furthermore, an empirical study of solutions to enable decoders to generate richer fine-grained output sentences is provided. Finally, the attention mechanism which is a technique to cope with long-term dependencies and to improve the encoder-decoder performance on sophisticated tasks is studied.