Evaluating neural network explanation methods using hybrid documents and morphological prediction.
Nina Pörner, Hinrich Schütze, Benjamin Roth · arXiv (Cornell University) · 2018
We propose two novel paradigms for evaluating neural network explanations in NLP. The first paradigm works on hybrid documents, the second exploits morphosyntactic agreements. Neither paradigm requires manual annotations; instead, a relevance ground truth is generated automatically. In our experiments, successful explanations for Long Short Term Memory networks (LSTMs) were produced by a decomposition of memory cells (Murdoch & Szlam, 2017), while for convolutional neural networks, a gradient-based method by (Denil et al., 2014) works well. We also introduce LIMSSE, a substring-based extension of LIME (Ribeiro et al., 2016) that produces the most successful explanations in the hybrid document experiment.