Recurrent neural networks (RNNs) have been successfully applied to various natural language processing (NLP) tasks and achieved better results than conventional methods. However, the lack of understanding of the mechanisms behind their effectiveness limits further improvements on their architectures. In this paper, we present a visual analytics method for understanding and comparing RNN models for NLP tasks. We propose a technique to explain the function of individual hidden state units based on their expected response to input texts. We then co-cluster hidden state units and words based on the expected response and visualize co-clustering results as memory chips and word clouds to provide more structured knowledge on RNNs' hidden states. We also propose a glyph-based sequence visualization based on aggregate information to analyze the behavior of an RNN's hidden state at the sentence-level. The usability and effectiveness of our method are demonstrated through case studies and reviews from domain experts.
We formulate the relation between hidden state units and discrete inputs of RNNs as bipartite graphs to investigate the structure of information stored hidden states. Co-clustering is a widely used method for analyzing bipartite graphs, which simultaneously clusters two kinds of entities in a graph @cite_1 . Some recent work combined co-clustering with visualization to assist intelligence analysis, where different types of entities are considered @cite_49 @cite_47 . A most recent work proposed by @cite_2 presented an interactive co-clustering visualization where cluster nodes are visualized as adjacency matrices or treemaps. Although both adjacency matrices and treemaps used in this visualization are well established, none could be adjusted to visualize abstract entities like hidden states.
@cite_47 @cite_1 @cite_49 @cite_2
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A large number of clustering approaches have been proposed for the analysis of gene expression data obtained from microarray experiments. However, the results from the application of standard clustering methods to genes are limited. This limitation is imposed by the existence of a number of experimental conditions where the activity of genes is uncorrelated. A similar limitation exists when clustering of conditions is performed. For this reason, a number of algorithms that perform simultaneous clustering on the row and column dimensions of the data matrix has been proposed. The goal is to find submatrices, that is, subgroups of genes and subgroups of conditions, where the genes exhibit highly correlated activities for every condition. In this paper, we refer to this class of algorithms as biclustering. Biclustering is also referred in the literature as coclustering and direct clustering, among others names, and has also been used in fields such as information retrieval and data mining. In this comprehensive survey, we analyze a large number of existing approaches to biclustering, and classify them in accordance with the type of biclusters they can find, the patterns of biclusters that are discovered, the methods used to perform the search, the approaches used to evaluate the solution, and the target applications. A prototype visual analytics tool uses data mining algorithms to find patterns in textual datasets and then supports exploration of these patterns in the form of biclusters on a high-resolution display. A bipartite graph models the relation between two different types of entities. It is applicable, for example, to describe persons' affiliations to different social groups or their association with subjects such as topics of interest. In these applications, it is important to understand the connectivity patterns among the entities in the bipartite graph. For the example of a bipartite relation between persons and their topics of interest, people may form groups based on their common interests, and the topics also can be grouped or categorized based on the interested audiences. Co-clustering methods can identify such connectivity patterns and find clusters within the two types of entities simultaneously. In this paper, we propose an interactive visualization design that incorporates co-clustering methods to facilitate the identification of node clusters formed by their common connections in a bipartite graph. Besides highlighting the automatically detected node clusters and the connections among them, the visual interface also provides visual cues for evaluating the homogeneity of the bipartite connections in a cluster, identifying potential outliers, and analyzing the correlation of node attributes with the cluster structure. The interactive visual interface allows users to flexibly adjust the node grouping to incorporate their prior knowledge of the domain, either by direct manipulation (i.e., splitting and merging the clusters), or by providing explicit feedback on the cluster quality, based on which the system will learn a parametrization of the co-clustering algorithm to better align with the users' notion of node similarity. To demonstrate the utility of the system, we present two example usage scenarios on real world datasets.