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Paper · 1603.07396 · 2016

A Diagram Is Worth A Dozen Images

arXiv · PDF · Open in the Atlas

Code that ran

We lifted 3 functions out of this paper's own repositories and ran 3 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.

RepositoryRoleRan
allenai/dqa-net canonical 3 of 3
FunctionStatusWhere it lives
anno2rels Ran allenai/dqa-net/prepro/p05.py
code served (permissive licence) · get_code("c70da098fa5d857d")
qa2hypo Ran allenai/dqa-net/prepro/p05.py
code served (permissive licence) · get_code("f1d5c3466f0df405")
rel2text Ran allenai/dqa-net/prepro/p05.py
code served (permissive licence) · get_code("166293f0580d3160")

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Abstract

Diagrams are common tools for representing complex concepts, relationships and events, often when it would be difficult to portray the same information with natural images. Understanding natural images has been extensively studied in computer vision, while diagram understanding has received little attention. In this paper, we study the problem of diagram interpretation and reasoning, the challenging task of identifying the structure of a diagram and the semantics of its constituents and their relationships. We introduce Diagram Parse Graphs (DPG) as our representation to model the structure of diagrams. We define syntactic parsing of diagrams as learning to infer DPGs for diagrams and study semantic interpretation and reasoning of diagrams in the context of diagram question answering. We devise an LSTM-based method for syntactic parsing of diagrams and introduce a DPG-based attention model for diagram question answering. We compile a new dataset of diagrams with exhaustive annotations of constituents and relationships for over 5,000 diagrams and 15,000 questions and answers. Our results show the significance of our models for syntactic parsing and question answering in diagrams using DPGs.

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