On the Readability of Deep Learning Models: the role of Kernel-based Deep Architectures
Authors: Danilo Croce, Daniele Rossini, Roberto Basili (Università di Roma Tor Vergata, 2018)
Topic: Language Models

Abstract
Deep Neural Networks achieve English. Deep Neural Networks achieve state-of-the-art performances in several semantic NLP tasks but lack of explanation capabilities as for the limited interpretability of the underlying acquired models. In other words, tracing back causal connections between the linguistic properties of an input instance and the produced classiﬁcation is not possible. In this paper, we propose to apply Layerwise Relevance Propagation over linguistically motivated neural architectures, namely Kernel-based Deep Architectures (KDA), to guide argumentations and explanation inferences. In this way, decisions provided by a KDA can be linked to the semantics of input examples, used to linguistically motivate the network output.

Introduction
Deep Neural Networks are usually criticized as they are not epistemologically transparent devices, i.e. their models cannot be used to provide explanations of the resulting inferences. An example can be neural question classiﬁcation (QC) (e.g. (Croce et al., 2017)). In QC the correct category of a question is detected to optimize the later stages of a question answering system, (Li and Roth, 2006). An epistemologically transparent learning system should trace back the causal connections between the proposed question category and the linguistic properties of the input question. For example, the system could motivate the decision: ”What is the capital of Zimbabwe?” refers to a Location, with a sentence such as: Since it is similar to ”What is the capital of California?” which also refers to a Location. Unfortunately, neural models, as for example Multilayer Perceptrons (MLP), Long Short-Term Memory Networks (LSTM), (Hochreiter and Schmidhuber, 1997), or even Attention-based Networks (Larochelle and Hinton, 2010), correspond to parameters that have no clear conceptual counterpart: it is thus difﬁcult to trace back the network components (e.g. neurons or layers in the resulting topology) responsible for the answer. In image classiﬁcation, Layerwise Relevance Propagation (LRP) (Bach et al., 2015) has been used to decompose backward across the MLP layers the evidence about the contribution of individual input fragments (i.e. pixels of the input images) to the ﬁnal decision. Evaluation against the MNIST and ILSVRC benchmarks suggests that LRP activates associations between input and output fragments, thus tracing back meaningful causal connections. In this paper, we propose the use of a similar mechanism over a linguistically motivated network architecture, the Kernel-based Deep Architecture (KDA), (Croce et al., 2017). Tree Kernels (Collins and Duffy, 2001) are here used to integrate syntactic/semantic information within a MLP network. We will show how KDA input nodes correspond to linguistic instances and by applying the LRP method we are able to trace back causal associations between the semantic classiﬁcation and such instances. Evaluation of the LRP algorithm is based on the idea that explanations improve the user expectations about the correctness of an answer and shows its applicability in human computer interfaces. In the rest of the paper, Section 2 describes the KDA neural approach while section 3 illustrates how LRP connects to KDAs. In section 4 early results of the evaluation are reported.

Conclusion
This paper describes an LRP application to a KDA that makes use of analogies as explanations of a neural network decision. A methodology to measure the explanation quality has been also proposed and the experimental evidence conﬁrms the effectiveness of the method in increasing the trust of a user upon automatic classiﬁcations. Future work will focus on the selection of subtrees as meaningful evidences for the explanation, or on the modeling of negative information for disambiguation as well as on more in depth investigation of the landmark selection policies. Moreover, improved experimental scenarios involving users and dialogues will be also designed, e.g. involving further investigation within Semantic Role Labeling, using the method proposed in (Croce et al., 2012).