14,049 research outputs found
Textbook Question Answering with Multi-modal Context Graph Understanding and Self-supervised Open-set Comprehension
In this work, we introduce a novel algorithm for solving the textbook
question answering (TQA) task which describes more realistic QA problems
compared to other recent tasks. We mainly focus on two related issues with
analysis of the TQA dataset. First, solving the TQA problems requires to
comprehend multi-modal contexts in complicated input data. To tackle this issue
of extracting knowledge features from long text lessons and merging them with
visual features, we establish a context graph from texts and images, and
propose a new module f-GCN based on graph convolutional networks (GCN). Second,
scientific terms are not spread over the chapters and subjects are split in the
TQA dataset. To overcome this so called "out-of-domain" issue, before learning
QA problems, we introduce a novel self-supervised open-set learning process
without any annotations. The experimental results show that our model
significantly outperforms prior state-of-the-art methods. Moreover, ablation
studies validate that both methods of incorporating f-GCN for extracting
knowledge from multi-modal contexts and our newly proposed self-supervised
learning process are effective for TQA problems.Comment: ACL2019 Camera-read
Generalized characteristic polynomials of graph bundles
In this paper, we find computational formulae for generalized characteristic
polynomials of graph bundles. We show that the number of spanning trees in a
graph is the partial derivative (at (0,1)) of the generalized characteristic
polynomial of the graph. Since the reciprocal of the Bartholdi zeta function of
a graph can be derived from the generalized characteristic polynomial of a
graph, consequently, the Bartholdi zeta function of a graph bundle can be
computed by using our computational formulae
Dynamic Graph Generation Network: Generating Relational Knowledge from Diagrams
In this work, we introduce a new algorithm for analyzing a diagram, which
contains visual and textual information in an abstract and integrated way.
Whereas diagrams contain richer information compared with individual
image-based or language-based data, proper solutions for automatically
understanding them have not been proposed due to their innate characteristics
of multi-modality and arbitrariness of layouts. To tackle this problem, we
propose a unified diagram-parsing network for generating knowledge from
diagrams based on an object detector and a recurrent neural network designed
for a graphical structure. Specifically, we propose a dynamic graph-generation
network that is based on dynamic memory and graph theory. We explore the
dynamics of information in a diagram with activation of gates in gated
recurrent unit (GRU) cells. On publicly available diagram datasets, our model
demonstrates a state-of-the-art result that outperforms other baselines.
Moreover, further experiments on question answering shows potentials of the
proposed method for various applications
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