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Input-Jacobian modulation analysis

Techniqueadvanced

Detects context-modifying words by comparing the input Jacobian evaluated after that word (h_mod) to the input Jacobian at the nearest line-attractor fixed point (h*), then fits a low-rank bilinear correction model to how the input Jacobian itself changes as a function of displacement off the attractor.

Used in (1 observation)

Negation and intensification deflect sentiment RNNs into an orthogonal 2D subspacemeasured
structure: Linear Subspace · models: LSTM (sentiment classification, Yelp/IMDB/SST), GRU (sentiment classification, Yelp/IMDB/SST), Update Gate RNN (sentiment classification, Yelp/IMDB/SST), Vanilla RNN (sentiment classification, Yelp/IMDB/SST) · paper: How Recurrent Networks Implement Contextual Processing in Sentiment Analysis