Beyond word importance: using contextual decompositions to extract interactions from LSTMs.
Abstract
The driving force behind the recent success of LSTMs has been their ability to
learn complex and non-linear relationships. Consequently, our inability to de-
scribe these relationships has led to LSTMs being characterized as black boxes.
To this end, we introduce contextual decomposition (CD), a novel algorithm for
capturing the contributions of combinations of words or variables in terms of CD
scores. On the task of sentiment analysis with the Yelp and SST data sets, we
show that CD is able to reliably identify words and phrases of contrasting senti-
ment, and how they are combined to yield the LSTM’s final prediction. Using the
phrase-level labels in SST, we also demonstrate that CD is able to successfully
extract positive and negative negations from an LSTM, something which has not
previously been done.
learn complex and non-linear relationships. Consequently, our inability to de-
scribe these relationships has led to LSTMs being characterized as black boxes.
To this end, we introduce contextual decomposition (CD), a novel algorithm for
capturing the contributions of combinations of words or variables in terms of CD
scores. On the task of sentiment analysis with the Yelp and SST data sets, we
show that CD is able to reliably identify words and phrases of contrasting senti-
ment, and how they are combined to yield the LSTM’s final prediction. Using the
phrase-level labels in SST, we also demonstrate that CD is able to successfully
extract positive and negative negations from an LSTM, something which has not
previously been done.
Research Areas
