The behavior of deep neural networks (DNNs) is hard to understand. This makes it necessary to explore post hoc explanation methods. We conduct the first comprehensive evaluation of explanation methods for NLP. To this end, we design two novel evaluation paradigms that cover two important classes of NLP problems: small context and large context problems. Both paradigms require no manual annotation and are therefore broadly applicable. We also introduce LIMSSE, an explanation method inspired by LIME that is designed for NLP. We show empirically that LIMSSE, LRP and DeepLIFT are the most effective explanation methods and recommend them for explaining DNNs in NLP. lrp the link provided by the editor above [encourages ...] grad dot s the link provided by the editor above [encourages ...] grad L2 s few if any events in history [are ...] decomp few if any events in history [are ...]
REF have investigated several of the previously discussed techniques and have found LRP and DeepLIFT to be the most effective approaches for explaining deep neural networks in NLP.
Evaluating neural network explanation methods using hybrid documents and morphosyntactic agreement
ACL 2018
Computer Science