1. Visualizing and Understanding Neural Models in NLP
Jiwei Li, Xinlei Chen, Eduard Hovy and Dan Jurafsky
Presentation by
Rohit Gupta
Roll No

2. Motivation
Vector-based models produced by applying neural networks to natural language are very difficult to interpret

3. Dataset used Stanford Sentiment Treebank dataset
sentiment labels for every parse tree constituent, from sentences to to individual words, for 11,855 sentences

4. Models studied
Standard recurrent sequence models with TANH activation functions
LSTMs: Long Short Term Memory
Bidirectional LSTMs

5. Visualizations Compositionality
Negation
Intensification
Concessive
Salience (contribution of unit to the final composed meaning)
Gradient back-propagation
The variance of a token from the average word node
LSTM-style gates that measure information flow

6. Local compositionality

7. t-SNE visualization on representations for negation

8. Concessive clause composition

9. Salience Method 1: First derivatives

10. Salience method 2: Variance of word from sentence mean

11. Salience method 3: Gate models