5 Important Papers in NLP that Everyone Should Read
Last Updated on July 6, 2022 by Editorial Team
Author(s): Rijul Singh Malik
Originally published on Towards AI the World’s Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses.
A blog highlighting important papers in the field of NLP
This blog is going to look at 5 papers in natural language processing that every machine learning enthusiast should read. Its been a while since I started reading papers, but I still do every so often. I thought this was a good opportunity to introduce ML/NLP enthusiasts to some of the core papers in the field.
1. The Birth of Word Vectors: Peters et al.
Much has been said about word2vec and its applications to Natural Language Processing. Few people know, however, that word2vec is not the first attempt to create word vectors. In fact, it is just one of many generations of word vector models. In this article, I will briefly describe the history of word vectors and provide links to their source material, so that you can explore them and understand the evolution of word vector models.
Word Vectors: The Birth of a New Dimension in Language Processing (Peters et al.) In this paper, Peters et al. propose a new approach to representing words by using a 300-dimensional space. This method can be used in a wide range of NLP applications such as word analogy tasks, word similarity, and word similarity. The paper describes how they can be used in practice and how they can be applied to different NLP applications.
2. The Rise of Recurrent Neural Networks for Language Understanding: Louis-Sanchez et al.
We investigate the use of recurrent neural networks (RNNs) for sentence classification, parsing, and generation. RNNs are well-suited to learn long-term dependencies, which is crucial for natural language processing tasks, but they have recently been shown to be vulnerable to catastrophic forgetting. We propose various approaches to address this issue that result in substantial improvements in accuracy. We also show that RNNs can achieve competitive performance on language modeling tasks, even for long input sequences.
Recent progress in neural networks has been made in the context of Natural Language Processing (NLP), where a recurrent neural network (RNN) is typically employed to represent the structure of the input sentence. This paper surveys a number of seminal RNN-based NLP tasks, including unsupervised sentiment classification, sentence compression, and paraphrase generation. The paper also discusses a number of RNN-related challenges and highlights recent work in the areas of temporal and contextual entailment and structured prediction.
Recurrent neural networks have recently been the focus of several new directions in Natural Language Processing (NLP). This paper explores the advantages and limitations of recurrent neural network architectures for language understanding. The authors compare recurrent neural network architectures to long short-term memory (LSTM) architectures, among others. They also discuss the advantages of recurrent neural networks for NLP.
3. Mastering the Game of Go with Deep Neural Networks and Tree Search: Silver et al.
Deep Learning is revolutionizing many fields of Machine Learning and Artificial Intelligence. In this paper, the authors describe a new approach to the problem of playing Go using Deep Neural Networks. Go is considered to be one of the hardest games for an artificial agent to play, due to a large number of potential board positions, the difficulty of evaluating board positions, and the need to consider which move to make next. The authors’ approach is to create a neural network that can search the space of possible board positions, and then use a second neural network to evaluate positions and select the move to make. This approach allows the program to explore many more moves than if it had to evaluate all the positions themselves. The authors trained their program using a combination of supervised learning and self-play.
On this page, we review the recent progress in beating the best human player of the game Go by computers. We first present an overview of the game, its historical significance, and a few notable milestones in the history of AI. We then discuss the neural network architecture of AlphaGo, its training methodology, and its hardware implementation. We also examine the details of the tree search function used in AlphaGo. Finally, we discuss the limitations of AlphaGo and the prospects for the future of AI.
4. Neural Machine Translation by Jointly Learning to Align and Translate: Mikolov et al.
This paper introduces the idea of neural machine translation. It shows that neural networks are capable of translating from one language to another (English to French and vice-versa) without requiring bilingual data. The idea is to first align the input sentences in both languages using a neural network, and then feed the aligned sentences to a neural network that translates from one language to another.
Recently, machine translation (MT) systems have achieved state-of-the-art performance on many language pairs, with the help of large parallel corpora and powerful convolutional neural networks. However, training these systems is expensive, often requiring weeks of CPU time. One way to speed up training while maintaining accuracy is to jointly learn to align and translate, which reduces the number of training examples required to learn the alignment and translation components. However, existing approaches to jointly learning alignment and translation lack a principled way to incorporate the alignment into the network architecture, which prevents them from capturing the structure of the alignment. In this paper, we propose to jointly learn to align and translate by simultaneously minimizing the statistical distance between alignment and translation, and a loss for misalignment. We propose a new loss for misalignment that is simple to train and provides state-of-the-art results on several language pairs.
5. Deep Neural Networks for Image Classification: Deng et al.
Deep learning is a buzzword that is thrown around in almost every business and website. But what does it really mean? What does it have to do with an improved website or a better experience for your visitors? Most people don’t realize how much their website affects their lives outside of the website, but an outstanding user experience (UX) can often lead to conversions. Deep learning is a family of learning algorithms that aim to emulate the structure of the brain. This post highlights five of the most important papers in Deep Learning for Natural Language Processing (NLP).
Deep learning has been a hot topic in the NLP community this year. Deep learning is a particular style of neural network that is said to be able to learn high-level functions from raw data. In this paper, the authors train a variety of neural networks (including recurrent neural networks) on a large dataset of images from ImageNet. They train a variety of networks and compare their performance on the validation set after training. They find that a deep neural network trained on a relatively small amount of data performs better than a variety of other neural networks such as recurrent neural networks, long short-term memory networks, and convolutional neural networks. The authors also find that the deep neural networks perform better than shallow neural networks. The authors suggest that the deep networks do well because they are able to identify visual concepts and that this ability is crucial to image classification.
These papers will help you understand the basics of NLP.
5 Important Papers in NLP that Everyone Should Read was originally published in Towards AI on Medium, where people are continuing the conversation by highlighting and responding to this story.
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