Title: Neural Machine Translation by Jointly Learning to Align and Translate
Date: 1 Sep 2014
Authors: Dzmitry Bahdanau, Kyunghyun Cho, Yoshua Bengio
Link: https://arxiv.org/abs/1409.0473

Summary:

This paper explores a novel approach to neural machine translation, called "RNNsearch", which uses a bidirectional recurrent neural network (BiRNN) to encode a source sentence into a sequence of vectors, allowing the decoder to selectively retrieve relevant information without relying on a fixed-length vector. This approach improves performance, especially when dealing with long sentences, achieving results comparable to conventional phrase-based statistical machine translation systems. The paper compares RNNsearch with the existing "RNNencdec" method, highlighting its advantages and demonstrating the effectiveness of the soft-alignment mechanism in capturing relationships between source and target words. The authors conclude by discussing the potential of RNNsearch for improving machine translation and advancing our understanding of natural language processing.

Key Topics:

Neural Machine Translation, Encoder-Decoder Architecture, Attention, Joint Learning, Alignment Model, Translation Performance