In the past few years, deep learning is all the fuss in the tech industry. To keep up on things I like to get my hands dirty implementing interesting network architectures I come across in article readings. Few months ago I came across a very nice article called Siamese Recurrent Architectures for Learning Sentence Similarity which offers a pretty straightforward approach at the common problem of sentence similarity. Named MaLSTM ("Ma" for Manhattan distance), its architecture is depicted in figure 1 (diagram excludes the sentence preprocessing part). Notice that since this is a Siamese network, it is easier to train because it shares weights on both sides.
Jul-17-2017, 09:35:26 GMT