How recurrent networks implement contextual processing in sentiment analysis
This work addresses the challenge of interpreting contextual processing in neural networks for researchers in machine learning and natural language processing, though it is incremental as it builds on existing methods for network analysis.
The authors tackled the problem of understanding how recurrent neural networks (RNNs) process context in sentiment analysis, and by reverse engineering RNNs, they developed methods to identify and quantify contextual effects, which enabled improvements to baseline models that recovered over 90% of the RNN's performance gain.
Neural networks have a remarkable capacity for contextual processing--using recent or nearby inputs to modify processing of current input. For example, in natural language, contextual processing is necessary to correctly interpret negation (e.g. phrases such as "not bad"). However, our ability to understand how networks process context is limited. Here, we propose general methods for reverse engineering recurrent neural networks (RNNs) to identify and elucidate contextual processing. We apply these methods to understand RNNs trained on sentiment classification. This analysis reveals inputs that induce contextual effects, quantifies the strength and timescale of these effects, and identifies sets of these inputs with similar properties. Additionally, we analyze contextual effects related to differential processing of the beginning and end of documents. Using the insights learned from the RNNs we improve baseline Bag-of-Words models with simple extensions that incorporate contextual modification, recovering greater than 90% of the RNN's performance increase over the baseline. This work yields a new understanding of how RNNs process contextual information, and provides tools that should provide similar insight more broadly.