10,349 research outputs found
OhioState at SemEval-2018 Task 7: Exploiting Data Augmentation for Relation Classification in Scientific Papers using Piecewise Convolutional Neural Networks
We describe our system for SemEval-2018 Shared Task on Semantic Relation
Extraction and Classification in Scientific Papers where we focus on the
Classification task. Our simple piecewise convolution neural encoder performs
decently in an end to end manner. A simple inter-task data augmentation
signifi- cantly boosts the performance of the model. Our best-performing
systems stood 8th out of 20 teams on the classification task on noisy data and
12th out of 28 teams on the classification task on clean data.Comment: To apperar in Proceedings of International Workshop on Semantic
Evaluation (SemEval-2018
Visual Question Answering: A Survey of Methods and Datasets
Visual Question Answering (VQA) is a challenging task that has received
increasing attention from both the computer vision and the natural language
processing communities. Given an image and a question in natural language, it
requires reasoning over visual elements of the image and general knowledge to
infer the correct answer. In the first part of this survey, we examine the
state of the art by comparing modern approaches to the problem. We classify
methods by their mechanism to connect the visual and textual modalities. In
particular, we examine the common approach of combining convolutional and
recurrent neural networks to map images and questions to a common feature
space. We also discuss memory-augmented and modular architectures that
interface with structured knowledge bases. In the second part of this survey,
we review the datasets available for training and evaluating VQA systems. The
various datatsets contain questions at different levels of complexity, which
require different capabilities and types of reasoning. We examine in depth the
question/answer pairs from the Visual Genome project, and evaluate the
relevance of the structured annotations of images with scene graphs for VQA.
Finally, we discuss promising future directions for the field, in particular
the connection to structured knowledge bases and the use of natural language
processing models.Comment: 25 page
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