Follow the WeChat public account “ML_NLP“ Set as “Starred“, heavy content delivered first-hand! Table of Contents: A Brief Review of ELMo and Transformer DAE and Masked Language Model Detailed Explanation of BERT Model Different Training Methods of BERT Model How to Apply BERT Model in Real Projects How to Slim Down BERT Problems with BERT … Read more
Author丨JayLou Zhihu Column丨High-energy NLP Journey Address丨https://zhuanlan.zhihu.com/p/76912493 This article summarizes and compares pre-trained language models in NLP in a Q&A format, covering three main aspects and the following models: Unidirectional feature representation autoregressive pre-trained language models, collectively referred to as unidirectional models: ELMO/ULMFiT/SiATL/GPT1.0/GPT2.0; Bidirectional feature representation autoencoding pre-trained language models, collectively referred to as BERT series … Read more
Author: PRATEEK JOSHI Translator: Han Guojun Proofreader: Li Hao This article is approximately 3500 words, and is recommended to be read in 15 minutes. This article will introduce the principles of ELMo and how it differs from traditional word embeddings, followed by practical demonstrations of its effectiveness. Introduction I am dedicated to researching issues related … Read more
For all source code in this tutorial, please visit Github: https://github.com/zlsdu/Word-Embedding 1. Word2vec 1. Gensim Library The gensim library provides implementations of the Word2vec cbow model and skipgram model, which can be called directly. Full reference code 2. TensorFlow Implementation of Skipgram Model The skipgram model predicts context words based on a center word; there … Read more
Selected from arXiv Authors:Eric Wallace et al. Translation by Machine Heart Contributors:Mo Wang Performing numerical reasoning on natural language text is a long-standing challenge for end-to-end models. Researchers from the Allen Institute for AI, Peking University, and the University of California, Irvine, attempt to explore whether “out-of-the-box” neural NLP models can solve this problem, and … Read more
Introduction Remember not long ago in the field of machine reading comprehension, where Microsoft and Alibaba surpassed humans on SQuAD with R-Net+ and SLQA respectively, and Baidu topped the MS MARCO leaderboard with V-Net while exceeding human performance on BLEU? These networks can be said to be increasingly complex, and it seems that the research … Read more
Natural Language Processing Author: Zhang Junlin Source: Deep Learning Frontier Notes Zhihu Column Original Link: https://zhuanlan.zhihu.com/p/49271699 The theme of this article is the pre-training process in natural language processing (NLP). It will roughly explain how pre-training techniques in NLP have gradually developed into the BERT model, naturally illustrating how the ideas behind BERT were formed, … Read more
Selected fromthe Gradient Author：Sebastian Ruder Translated by Machine Heart In the field of computer vision, models pre-trained on ImageNet are commonly used for various CV tasks such as object detection and semantic segmentation. In contrast, in the field of natural language processing (NLP), we typically only use pre-trained word embedding vectors to encode the relationships … Read more
Excerpt from arXiv Author: Noah A. Smith Translated by Machine Heart Contributors: Panda The basics of natural language processing involve the representation of words. Noah Smith, a professor of Computer Science and Engineering at the University of Washington, recently published an introductory paper on arXiv that explains how words are processed and represented in natural … Read more
Recently, in the field of Natural Language Processing (NLP), the use of pre-training methods for language models has achieved significant improvements across various NLP tasks, attracting widespread attention from various sectors. In this article, I will summarize some relevant papers I have recently read, selecting a few representative models (including ELMo [1], OpenAI GPT [2], … Read more