Explaining How a Neural Network Play the Go Game and Let People Learn

The AI model has surpassed human players in the game of Go, and it is widely believed that the AI model has encoded new knowledge about the Go game beyond human players. In this way, explaining the knowledge encoded by the AI model and using it to teach human players represent a promising-yet-challenging issue in explainable AI. To this end, mathematical supports are required to ensure that human players can learn accurate and verifiable knowledge, rather than specious intuitive analysis. Thus, in this paper, we extract interaction primitives between stones encoded by the value network for the Go game, so as to enable people to learn from the value network. Experiments show the effectiveness of our method.

Concept-Level Explanation for the Generalization of a DNN

This paper explains the generalization power of a deep neural network (DNN) from the perspective of interactive concepts. Many recent studies have quantified a clear emergence of interactive concepts encoded by the DNN, which have been observed on different DNNs during the learning process. Therefore, in this paper, we investigate the generalization power of each interactive concept, and we use the generalization power of different interactive concepts to explain the generalization power of the entire DNN. Specifically, we define the complexity of each interactive concept. We find that simple concepts can be better generalized to testing data than complex concepts. The DNN with strong generalization power usually learns simple concepts more quickly and encodes fewer complex concepts. More crucially, we discover the detouring dynamics of learning complex concepts, which explain both the high learning difficulty and the low generalization power of complex concepts.

Interpreting Hierarchical Linguistic Interactions in DNNs

This paper proposes a method to disentangle and quantify interactions among words that are encoded inside a DNN for natural language processing. We construct a tree to encode salient interactions extracted by the DNN. Six metrics are proposed to analyze properties of interactions between constituents in a sentence. The interaction is defined based on Shapley values of words, which are considered as an unbiased estimation of word contributions to the network prediction. Our method is used to quantify word interactions encoded inside the BERT, ELMo, LSTM, CNN, and Transformer networks. Experimental results have provided a new perspective to understand these DNNs, and have demonstrated the effectiveness of our method.