Chapter 1:Representation Learning and NLP 传统的机器学习方法包含三个模块: 表征 目标 优化 可以理解为算法的输入,算法的评估,参数的优化 对于深度学习,有两种feature:1)Distributed 表示 2)Deep结构 NLP之中特征表征的几大难点:1)Multiple Granularities 多粒度 2)Multiple Tasks 下游任务很多很复杂 3)Multiple Dom...
Conventional Natural Language Processing (NLP) heavily relies on feature engineering, which requires careful design and considerable expertise. Representation learning aims to learn representations of raw data as useful information for further classification or prediction. This chapter presents a brief ...
监督学习 Supervised learning,需要大量的标注数据来训练神经网络模型,利用模型的预测和数据的真实标签的cross-entropy损失进行反向传播,完成模型训练之后,移除最后一层fc层,把模型的输出作为数据的Representation。 无监督学习 Unsupervised learning,如主成分分析(PCA)及自编码器(Autoencoder)通过对输入数据进行维度压缩,从而...
self-supervised learning has been a first-class citizen in NLP research for quite a while. Language Models have existed since the 90’s even before the phrase “self-supervised learning” was termed. The Word2Vec paper from 2013 popularized this paradigm and the field has rapidly...
For different NLP scenarios, different types of neural networks can be carefully assembled to mimic human's behavioral patterns of understanding the real world or to exploit the semantics based on linguistic composition. This thesis focuses on representation learning at different granularities; different ...
In Proceed- ings of the 1st Workshop on Representation Learning for NLP. ... E Triantafillou,JR Kiros,R Urtasun,... - Meeting of the Association for Computational Linguistics 被引量: 0发表: 2016年 A Joint Model for Word Embedding and Word Morphology abs/1607.05368, 2016.Jey Han Lau and ...
On the flip side, supervised neural nets are often used to learn representations. One example is Collobert-Weston networks [4], which attempt to solve a number of supervised NLP tasks by learning representations which are shared between them. Some of the tasks are fairly simple and have a lar...
一、DeepNLP的核心关键:语言表示(Representation) 最近有一个新名词:Deep Learning + NLP = DeepNLP。当常规的机器学习Machine Learning升级发展到了一定的阶段后,慢慢的被后起的深度学习Deep Learning夺势而去,并如火如荼地引领了一波新高潮,因为Deep Learning有machinelearning过而不及之处!那当Deep Learning进入自...
UniSpeech-SAT - Universal Speech Representation Learning with Speaker Aware Pre-Training Join 'Speech and Language Technologies' Meetup group https://www.meetup.com/speech-and-language-technology-meetup-group/ 人工智能 公开课 科技 计算机技术 科学研究 NLP 音频 自然语言处理 语音 语音技术 ...
19年末,NLP领域的Transformer进一步应用于Unsupervised representation learning,产生后来影响深远的BERT和GPT系列模型,反观CV领域,ImageNet刷到饱和,似乎遇到了怎么也跨不过的屏障。就在CV领域停滞不前的时候,Kaiming He带着MoCo横空出世,横扫了包括PASCAL VOC和COCO在内的7大数据集,至此,CV拉开了Self-Supervised研究新篇章...