Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Most existing Neural Machine Translation models use groups of characters or whole words as their unit of input and output.  propose a model with a hierarchical char2word encoder, that takes individual characters both as input and output.  first argue that this hierarchical representation of the character encoder reduces computational complexity, and show that it improves translation performance. Secondly, by qualitatively studying attention plots from the decoder  find that the model learns to compress common words into a single embedding whereas rare words, such as names and places, are represented character by character.
['Alexander Rosenberg Johansen', 'Jonas Meinertz Hansen', 'Elias Khazen Obeid', 'Casper Kaae Sønderby', 'Ole Winther']
I am writing a research paper on a idea Neural Machine Translation with Characters and Hierarchical Encoding
[{"from": "human", "value": "I am writing a research paper on a idea Neural Machine Translation with Characters and Hierarchical Encoding"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Most existing Neural Machine Translation models use groups of characters or whole words as their unit of input and output.  propose a model with a hierarchical char2word encoder, that takes individual characters both as input and output.  first argue that this hierarchical representation of the character encoder reduces computational complexity, and show that it improves translation performance. Secondly, by qualitatively studying attention plots from the decoder  find that the model learns to compress common words into a single embedding whereas rare words, such as names and places, are represented character by character."}]