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

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

Deep Learning Models to Study Sentence Comprehension in the Human Brain

Recent artificial neural networks that process natural language achieve unprecedented performance in tasks requiring sentence-level understanding. As such, they could be interesting models of the integration of linguistic information in the human brain. We review works that compare these artificial language models with human brain activity and we assess the extent to which this approach has improved our understanding of the neural processes involved in natural language comprehension. Two main results emerge. First, the neural representation of word meaning aligns with the context-dependent, dense word vectors used by the artificial neural networks. Second, the processing hierarchy that emerges within artificial neural networks broadly matches the brain, but is surprisingly inconsistent across studies. We discuss current challenges in establishing artificial neural networks as process models of natural language comprehension. We suggest exploiting the highly structured representational geometry of artificial neural networks when mapping representations to brain data

Neural Network With Nlp

This thesis is about neural networks and how their algorithmic systems work. Neural networks are well-suited to aiding people with complex challenges in real-world situations. Thesis topics include nonlinear and complicated interactions between inputs and outputs, as well as making inferences, discovering hidden links, patterns, and predictions, and modeling highly volatile data and variations to forecast uncommon events. Neural networks have the potential to help people make better decisions. NLP is a technique for analyzing, interpreting, and comprehending large amounts of text. We can no longer evaluate the text using traditional approaches due to the massive volumes of text data and the exceedingly unstructured data source, which is where NLP comes in. As a result, the research focuses on what a neural network is and how different types of neural networks are used in natural language processing. NLP (natural language processing) is a method for analyzing, interpreting, and comprehending vast amounts of text. Due to the huge volumes of text data and the extremely unstructured data source, we can no longer analyze the text using standard approaches, which is where NLP comes in. As a result, the study concentrates on what a neural network is and how various types of neural networks are used in natural language processing. Due to their exceptional success in numerous NLP tasks, BERT in particular has gotten a lot of attention. Google\u27s Bidirectional Encoder Representations from Transformers (BERT) is a Transformer-based machine learning methodology for pre-training in natural language processing (NLP)

Dependency Parsing with Dilated Iterated Graph CNNs

Dependency parses are an effective way to inject linguistic knowledge into many downstream tasks, and many practitioners wish to efficiently parse sentences at scale. Recent advances in GPU hardware have enabled neural networks to achieve significant gains over the previous best models, these models still fail to leverage GPUs' capability for massive parallelism due to their requirement of sequential processing of the sentence. In response, we propose Dilated Iterated Graph Convolutional Neural Networks (DIG-CNNs) for graph-based dependency parsing, a graph convolutional architecture that allows for efficient end-to-end GPU parsing. In experiments on the English Penn TreeBank benchmark, we show that DIG-CNNs perform on par with some of the best neural network parsers.Comment: 2nd Workshop on Structured Prediction for Natural Language Processing (at EMNLP '17