# Attention weights for identifying influence

This question concerns the application of self-attention weights for identifying influence of words in sentences.

For instance, we are performing a classification task on a set of sentences (e.g. sentiment analysis). The model takes the embedded words and feeds them into a (bidirectional) LSTM or GRU layer. The hidden states are combined by an attention vector to obtain a sentence representation that is used as an input for the classification.

For a set of sentences $$S$$, and the attention weights $$\alpha^s = <\alpha^s_1, \alpha^s_2, \ldots, \alpha^s_n>$$ for each sentence $$s \in S$$, we can combine attentions of all sentences into a matrix size $$|W| \times |S|$$ where $$|W|$$ is the vocabulary size and $$|S|$$ is the number of sentences, with column sum equal to 1 (each column is a probability distribution over words). On the other hand, sentence-class relationship forms a matrix of $$|C| \times |S|$$, where $$|C|$$ is the number of classes with each column a probability distribution over classes.

Using the above two learned probability distributions, is it possible to answer the following two questions:

1) For a particular class (e.g., excitement), what are the most influential words.

2) For a particular word, how much it influences a particular class. e.g. how much the word despise influence hatred?

Any pointers or references to similar work is highly appreciated.