Added key points to InferSent notes

notes/supervised-learning-of-universal-sentence-representations.md

@@ -2,3 +2,13 @@
 
 TLDR; The authors show that supervised training on the NLI task can produce high-quality "universal" sentence embeddings which outperform other existing models, on transfer tasks. They train the sentence vectors on the SNLI corpus using 4 different sentence encoding model architectures.
 
+### Key Points
+- The SNLI corpus is a large corpus of sentence pairs that have been manually categories into 3 classes: entailment, contradiction, and neutral. The SNLI task is good for learning sentence vectors because it forces the model to learn semantic representations
+
+- The 4 sentence encoding architectures used are:
+    - LSTM/GRU: Essentially the encoder of a seq2seq model
+    - BiLSTM: Bi-directional LSTM where each dim of the two (forwards and backwards) encoding are either summed or max-pooled
+    - Self-attentive network:  Weighted linear combination (Attention) over each hidden state vectors of a BiLSTM
+    - Hierarchical ConvNet: The authors introduce a variation of the AdaSent model, where at each layer of the CNN, a max pool is taken over the feature maps. Each of these max pooled vectors are concatenated to obtain the final sentence encoding.
+
+- The trained models are used to get sentence representations for different tasks such as classification (eg: sentiment analysis, Subj/obj), entailment (eg: SICK dataset), caption-image retrieval and a few other tasks.