Learning Convolutional Text Representations for Visual Question Answering

Visual question answering is a recently proposed artificial intelligence task that requires a deep understanding of both images and texts. In deep learning, images are typically modeled through convolutional neural networks, and texts are typically modeled through recurrent neural networks. While the requirement for modeling images is similar to traditional computer vision tasks, such as object recognition and image classification, visual question answering raises a different need for textual representation as compared to other natural language processing tasks. In this work, we perform a detailed analysis on natural language questions in visual question answering. Based on the analysis, we propose to rely on convolutional neural networks for learning textual representations. By exploring the various properties of convolutional neural networks specialized for text data, such as width and depth, we present our "CNN Inception + Gate" model. We show that our model improves question representations and thus the overall accuracy of visual question answering models. We also show that the text representation requirement in visual question answering is more complicated and comprehensive than that in conventional natural language processing tasks, making it a better task to evaluate textual representation methods. Shallow models like fastText, which can obtain comparable results with deep learning models in tasks like text classification, are not suitable in visual question answering.Comment: Conference paper at SDM 2018. https://github.com/divelab/sva

Do Neural Nets Learn Statistical Laws behind Natural Language?

The performance of deep learning in natural language processing has been spectacular, but the reasons for this success remain unclear because of the inherent complexity of deep learning. This paper provides empirical evidence of its effectiveness and of a limitation of neural networks for language engineering. Precisely, we demonstrate that a neural language model based on long short-term memory (LSTM) effectively reproduces Zipf's law and Heaps' law, two representative statistical properties underlying natural language. We discuss the quality of reproducibility and the emergence of Zipf's law and Heaps' law as training progresses. We also point out that the neural language model has a limitation in reproducing long-range correlation, another statistical property of natural language. This understanding could provide a direction for improving the architectures of neural networks.Comment: 21 pages, 11 figure

Hebbian learning in recurrent neural networks for natural language processing

This research project examines Hebbian learning in recurrent neural networks for natural language processing and attempts to interpret language at the level of a two and one half year old child. In this project five neural networks were built to interpret natural language: a Simple Recurrent Network with Hebbian Learning, a Jordan network with Hebbian learning and one hidden layer, a Jordannetwork with Hebbian learning and no hidden layers, a Simple Recurrent Network back propagation learning, and a nonrecurrent neural network with backpropagation learning. It is known that Hebbian learning works well when the input vectors are orthogonal, but, as this project shows, it does not perform well in recurrent neural networks for natural language processing when the input vectors for the individual words are approximately orthogonal. This project shows that,given approximately orthogonal vectors to represent each word in the vocabulary the input vectors for a given command are not approximately orthogonal and the internal representations that the neural network builds are similar for different commands. As the data shows, the Hebbian learning neural networks were unable to perform the natural language interpretation task while the back propagation neural networks were much more successful. Therefore, Hebbian learning does not work well in recurrent neural networks for natural language processing even when the input vectors for the individual words are approximately orthogonal

Networks and Natural Language Processing

Article discussing networks and natural language processing. The authors present some of the most successful graph-based representations and algorithms used in language processing and try to explain how and why they work

Natural Language Processing: A Look Into How Computers Understand Human Language

The semantic interpretation of the human language is very complex and diverse making natural language processing an interesting task for researchers and engineers. Natural language processing is a subfield of machine learning focusing on enabling computers to understand and process human languages. Although computers do not have the same intuitive understanding of natural language like humans do, recent advances in machine learning have enabled computers to perform many useful things with natural language like text classification, language modeling, speech recognition, and question answering. Computers are able to accomplish these tasks by learning the deep contextual representations of words including both the syntax and semantics. Through the use of recurrent neural networks, long short-term memory units, temporal convolution networks, and different language embedding models, computers have made significant strides in their ability to interpret and understand human language. With large volumes of textual data available and the need to structure the unstructured data source that is human language, the area of natural language processing will continue to be of interest.https://ecommons.udayton.edu/stander_posters/2706/thumbnail.jp