Universal sentence encoder
Ok so I might be a bit late to join the NLP bus but am so glad I boarded! Lets start with the universal sentence encoder. The universal sentence encoder was released a couple of years ago by Google and is widely appreciated by the NLP community as a quick way to generate a sentence embedding before any further processing can be done on it. One reason to not to use a naive encoding scheme based upon term frequency is that it ignores word ordering and can have high similarity even when the meaning of the sentence is not the same. Some examples mentioned in the blog below shows that the sentence it is cool and it and is it cool have a high similarity. The original paper mentions two ways to encode the natural language sentence - a) Transformer encoder - This consists of 6 stacked transformer layers (each has a self-attention module followed by a feed-forward network).The self attention takes care of the nearby context to generate the word embeddings. b) Deep averaging network - be