From fdbc86892c954300bc4401cb2dc31273386bda44 Mon Sep 17 00:00:00 2001 From: km601 <958747457@qq.com> Date: Fri, 8 Oct 2021 09:48:10 +0800 Subject: [PATCH] =?UTF-8?q?Bert=20=E7=B3=BB=E5=88=97=E6=A8=A1=E5=9E=8B=20?= =?UTF-8?q?=E6=9B=B4=E6=96=B0?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .gitignore | 53 ++ ...726e6e3538393538727172723472312e6a7067.gif | Bin 0 -> 272498 bytes .../bert_big/img/20201015170627.png | Bin 0 -> 44495 bytes .../bert_big/img/20201026153603.png | Bin 0 -> 5473 bytes .../bert_big/img/20201026153725.png | Bin 0 -> 76443 bytes .../bert_big/img/20201026154010.png | Bin 0 -> 81420 bytes .../bert_big/img/20201026154623.png | Bin 0 -> 91156 bytes ...46\210\252\345\233\276_20210910210322.png" | Bin 0 -> 44777 bytes ...46\210\252\345\233\276_20210910210729.png" | Bin 0 -> 52939 bytes ...46\210\252\345\233\276_20210910210907.png" | Bin 0 -> 62709 bytes ...46\210\252\345\233\276_20210910211607.png" | Bin 0 -> 272634 bytes 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100644 NLPinterview/PreTraining/bert_big/img/20201026153603.png create mode 100644 NLPinterview/PreTraining/bert_big/img/20201026153725.png create mode 100644 NLPinterview/PreTraining/bert_big/img/20201026154010.png create mode 100644 NLPinterview/PreTraining/bert_big/img/20201026154623.png create mode 100644 "NLPinterview/PreTraining/bert_big/img/\345\276\256\344\277\241\346\210\252\345\233\276_20210910210322.png" create mode 100644 "NLPinterview/PreTraining/bert_big/img/\345\276\256\344\277\241\346\210\252\345\233\276_20210910210729.png" create mode 100644 "NLPinterview/PreTraining/bert_big/img/\345\276\256\344\277\241\346\210\252\345\233\276_20210910210907.png" create mode 100644 "NLPinterview/PreTraining/bert_big/img/\345\276\256\344\277\241\346\210\252\345\233\276_20210910211607.png" create mode 100644 "NLPinterview/PreTraining/bert_big/img/\345\276\256\344\277\241\346\210\252\345\233\276_20210910212046.png" create mode 100644 "NLPinterview/PreTraining/bert_big/img/\345\276\256\344\277\241\346\210\252\345\233\276_20210910212205.png" create mode 100644 "NLPinterview/PreTraining/bert_big/img/\345\276\256\344\277\241\346\210\252\345\233\276_20210910212307.png" create mode 100644 "NLPinterview/PreTraining/bert_big/img/\345\276\256\344\277\241\346\210\252\345\233\276_20210910213336.png" create mode 100644 "NLPinterview/PreTraining/bert_big/img/\345\276\256\344\277\241\346\210\252\345\233\276_20210910213420.png" create mode 100644 "NLPinterview/PreTraining/bert_big/img/\345\276\256\344\277\241\346\210\252\345\233\276_20210910213837.png" create mode 100644 "NLPinterview/ner/crf/img/\345\276\256\344\277\241\346\210\252\345\233\276_20210901095003.png" diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..f3c7831 --- /dev/null +++ b/.gitignore @@ -0,0 +1,53 @@ +# Local .terraform directories +**/.terraform/* +**/w2v_model/ +**/saved/ +**/runs/* +**/.idea/ +**/__pycache__/ +**/.ipynb_checkpoints/ +**/summarys/ +model/ +**/model/ +**/models/ +**/outputs/ +**/saved_models/ +version/ +**/version/ +**/gitinfo/ +**/mywork/* + +T1_Relation_Classification_via_CDNN/saved_models/* + +text_simple_study/RE2_study/simple-effective-text-matching-pytorch/* + +# .tfstate files +*.tfstate +*.tfstate.* +*checkpoint +**/model.ckpt* +*.ckpt* +# .tfvars files +*.tfvars +*.all +*.hdf5 +*.json +*.txt +*.csv +task/.idea/* +*.xml +*.zip +*.rar +*.bin +*.vec +*.model +*.gz +*.bz2 +*.word +*.pkl +*.tsv +*.neg +*.pos +**/mywork/* +**/pdf/* + diff --git a/DeepLearningAlgorithm/cnn/img/68747470733a2f2f69322e6b6b6e6577732e63632f5349473d327374667567352f6374702d767a6e74722f713072306e36383872707337343832326f726e6e3538393538727172723472312e6a7067.gif b/DeepLearningAlgorithm/cnn/img/68747470733a2f2f69322e6b6b6e6577732e63632f5349473d327374667567352f6374702d767a6e74722f713072306e36383872707337343832326f726e6e3538393538727172723472312e6a7067.gif new file mode 100644 index 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+> 个人github:https://github.com/km1994/nlp_paper_study
+> 原文地址:https://github.com/km1994/nlp_paper_study/bert_study/
+> NLP百面百搭:https://github.com/km1994/NLP-Interview-Notes
+> 【注:手机阅读可能图片打不开!!!】 + +## 目录 + +- [【关于 Bert->XLNet->RoBERTa->ELECTRA->ERNIE 1.0->ERNIE 2.0->ERNIE-T 那些的你不知道的事](#关于-bert-xlnet-roberta-electra-ernie-10-ernie-20-ernie-t-那些的你不知道的事) + - [目录](#目录) + - [总结](#总结) + - [一、Bert](#一bert) + - [1.1 【BERT】介绍](#11-bert介绍) + - [1.2 【BERT】Bert 预训练任务?](#12-bertbert-预训练任务) + - [1.2.1 【BERT】任务介绍](#121-bert任务介绍) + - [1.2.2 【MLM】Bert 预训练任务 Masked LM 怎么做?](#122-mlmbert-预训练任务-masked-lm-怎么做) + - [1.2.2.1【MLM】动机](#1221mlm动机) + - [1.2.2.2【MLM】做法](#1222mlm做法) + - [1.2.3【NSP】Bert 预训练任务 Next Sentence Prediction 怎么做?](#123nspbert-预训练任务-next-sentence-prediction-怎么做) + - [1.2.3.1 【NSP】动机](#1231-nsp动机) + - [1.2.3.2 【NSP】方法](#1232-nsp方法) + - [1.2.3.3【NSP】操作](#1233nsp操作) + - [1.3 【Bert】问题](#13-bert问题) + - [1.3.1 【Bert】问题 1:预训练和微调之间的不匹配](#131-bert问题-1预训练和微调之间的不匹配) + - [1.3.2 【Bert】问题2:收敛速度慢问题](#132-bert问题2收敛速度慢问题) + - [二、XLNet](#二xlnet) + - [2.1 【XLNet】 介绍](#21-xlnet-介绍) + - [2.2 【XLNet】动机](#22-xlnet动机) + - [2.3 【XLNet】AR vs AE](#23-xlnetar-vs-ae) + - [2.4 【XLNet】 预训练](#24-xlnet-预训练) + - [2.4.1 【XLNet】Permutation Language Modeling](#241-xlnetpermutation-language-modeling) + - [2.4.2 【XLNet】 Two-Stream Self-Attention for Target-Aware Representations](#242-xlnet-two-stream-self-attention-for-target-aware-representations) + - [2.4.2.1 介绍](#2421-介绍) + - [2.4.2.2 计算两个self-attention](#2422-计算两个self-attention) + - [2.4.2.3 Transformer-XL](#2423-transformer-xl) + - [三、RoBERTa](#三roberta) + - [3.1 【RoBERTa】动机](#31-roberta动机) + - [3.2 【RoBERTa】工作](#32-roberta工作) + - [3.3 【RoBERTa】预训练](#33-roberta预训练) + - [3.3.1 动态掩码](#331-动态掩码) + - [3.3.2 去掉NSP任务](#332-去掉nsp任务) + - [3.3.3 更大的数据量](#333-更大的数据量) + - [3.3.4 更大的batch_size](#334-更大的batch_size) + - [3.3.5 Byte-level BPE编码](#335-byte-level-bpe编码) + - [四、ELECTRA](#四electra) + - [4.1 【ELECTRA】动机](#41-electra动机) + - [4.2 【ELECTRA】介绍](#42-electra介绍) + - [4.3 【ELECTRA】核心任务](#43-electra核心任务) + - [4.4 【ELECTRA】判别器 & 生成器](#44-electra判别器--生成器) + - [4.5 【ELECTRA】算法流程](#45-electra算法流程) + - [五、ERNIE 1.0](#五ernie-10) + - [5.1 【ERNIE 1.0】动机](#51-ernie-10动机) + - [5.2 【ERNIE 1.0】预训练](#52-ernie-10预训练) + - [5.2.1 Knowledge Integration](#521-knowledge-integration) + - [5.2.2 Dialogue Language Model(DLM)](#522-dialogue-language-modeldlm) + - [六、ERNIE 2.0](#六ernie-20) + - [6.1 【ERNIE 2.0】动机](#61-ernie-20动机) + - [6.2 【ERNIE 2.0】架构](#62-ernie-20架构) + - [6.3 【ERNIE 2.0】预训练](#63-ernie-20预训练) + - [6.3.1 预训练连续学习](#631-预训练连续学习) + - [6.3.2 更多的无监督预训练任务](#632-更多的无监督预训练任务) + - [七、ERNIE-T](#七ernie-t) + - [7.1 【ERNIE-T】动机](#71-ernie-t动机) + - [6.2 【ERNIE-T】架构](#62-ernie-t架构) + - [6.3 【ERNIE-T】预训练](#63-ernie-t预训练) + - [6.3.1 引入知识图谱](#631-引入知识图谱) + - [6.3.2 异构信息融合](#632-异构信息融合) + - [6.3.3 dAE](#633-dae) + - [参考](#参考) + +## 总结 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
名称介绍动机预训练方法微调问题
BertTransformer的双向编码器多义词问题 && GPT 单向 TransformerTask 1:Masked LM && Task 2:Next Sentence Prediction直接利用 特定任务数据 微调1. [MASK]预训练和微调之间的不匹配
2. Max Len 为 512
XLNet广义自回归预训练方法1. Bert 预训练和微调之间的不匹配
2. Bert 的 Max Len 为 512
(1) Permutation Language Modeling【解决Bert 预训练和微调之间的不匹配】
(2)Two-Stream Self-Attention for Target-Aware Representations【解决PLM出现的目标预测歧义】
(3)XLNet将最先进的自回归模型Transformer-XL的思想整合到预训练中【解决 Bert 的 Max Len 为 512】
直接利用 特定任务数据 微调问题
RoBERTaA Robustly Optimized BERT Pretraining Approach 1. 确定方法的哪些方面贡献最大可能是具有挑战性的
2. 训练在计算上是昂贵的的,限制了可能完成的调整量
1. 去掉下一句预测(NSP)任务
2. 动态掩码
3. 文本编码
4. 更大的数据量
5. 更大的batch_size
直接利用 特定任务数据 微调问题
ELECTRA判别器 & 生成器1. 只有15%的输入上是会有loss利用一个基于MLM的Generator来替换example中的某些个token,然后丢给Discriminator来判别直接利用 特定任务数据 微调问题
ERNIE 1.0ERNIE: Enhanced Representation through Knowledge Integration随机【MASK】会让模型不能充分学习到语义信息1. Knowledge Integration
2. Dialogue Language Model(DLM)
--问题
ERNIE 2.0ERNIE 2.0: A Continual Pre-Training Framework for Language Understanding之前的工作主要**通过词或句子的共现信号,构建语言模型任务进行模型预训练**。**除了语言共现信息之外,语料中还包含词法、语法、语义等更多有价值的信息**。1. 预训练连续学习
2. 更多的无监督预训练任务
--问题
ERNIE-TERNIE: Enhanced Language Representation with Informative Entities引入外部图谱1. 引入知识图谱
2. 异构信息融合
2. dAE
--问题
+ +## 一、Bert + +> 论文名称:《BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding》
+> 论文:https://arxiv.org/pdf/1810.04805.pdf
+> 代码:https://github.com/google-research/bert
+ +### 1.1 【BERT】介绍 + +BERT(Bidirectional Encoder Representations from Transformers)是一种Transformer的双向编码器,旨在通过在左右上下文中共有的条件计算来预先训练来自无标号文本的深度双向表示。因此,经过预先训练的BERT模型只需一个额外的输出层就可以进行微调,从而为各种自然语言处理任务生成最新模型。 + +### 1.2 【BERT】Bert 预训练任务? + +#### 1.2.1 【BERT】任务介绍 + +- 预训练 包含 两个 Task: + - Task 1:Masked LM + - Task 2:Next Sentence Prediction + +#### 1.2.2 【MLM】Bert 预训练任务 Masked LM 怎么做? + +##### 1.2.2.1【MLM】动机 +- 双向模型 由于 可以分别 从左到右 和 从右到左 训练,使得 每个词 都能 通过多层 上下文 “看到自己”; + +##### 1.2.2.2【MLM】做法 +- s1:随机遮蔽输入词块的某些部分; +- s2:仅预测那些被遮蔽词块; +- s3:被遮盖的标记对应的最终的隐藏向量被当作softmax的关于该词的一个输出,和其他标准语言模型中相同 + +#### 1.2.3【NSP】Bert 预训练任务 Next Sentence Prediction 怎么做? + +##### 1.2.3.1 【NSP】动机 + +很多重要的下游任务,例如问答(QA)和自然语言推理(NLI),都是基于对两个文本句子间关系的理解,而这种关系并非通过语言建模直接获得 + +##### 1.2.3.2 【NSP】方法 + +预训练 一个 二值化 NSP 任务 学习 句子间关系; + +##### 1.2.3.3【NSP】操作 +- 选择句子A和B作为预训练样本:B有50%的可能是A的下一句,也有50%的可能是来自语料库的随机句子 +> 举例:
+> - 输入=[CLS]男子去[MASK]商店[SEP]他买了一加仑[MASK]牛奶[SEP]
+> - Label= IsNext
+> - 输入=[CLS]男人[面具]到商店[SEP]企鹅[面具]是飞行##少鸟[SEP]
+> - Label= NotNext
+ +### 1.3 【Bert】问题 + +#### 1.3.1 【Bert】问题 1:预训练和微调之间的不匹配 +1. 解释:在微调期间从未看到[MASK]词块 +2. 解决方法: +- 以一定概率用 [MASK] 词块替换“遮蔽”单词,论文采用 15% 的概率 随机选择 词块 +- 目的:模型需要学习每个输入词块的分布式语境表征 +- 举例: + +> 句子:我的狗是毛茸茸的
+> 操作:
+> - 80%的概率:用[MASK]词块替换单词,例如,我的狗是毛茸茸的!我的狗是[MASK];
+> - 10%的概率:用随机词替换遮蔽词,例如,我的狗是毛茸茸的!我的狗是苹果;
+> - 10%的概率:保持单词不变,例如,我的狗是毛茸茸的!我的狗毛茸茸的。【目的:是将该表征偏向于实际观察到的单词】
+ +#### 1.3.2 【Bert】问题2:收敛速度慢问题 +1. 原因:每 batch 中只预测了15%的词块,导致 收敛速度慢 + +## 二、XLNet + +> 论文名称:《XLNet: Generalized Autoregressive Pretraining for Language Understanding》
+> 论文:https://arxiv.org/abs/1906.08237
+> 代码:https://github.com/zihangdai/xlnet
+ +### 2.1 【XLNet】 介绍 + +本文结合AR LM和AE LM,在Transformer-XL的基础上提出generalized autoregressive method,XLNet。 + +- 所有的分解序列作为一个集合,从中采样一个序列,XLNet按照AR LM的计算方式最大化有关序列所有可能的因式分解的排列的对数似然函数的期望。通常,当前token的上文包含left和right的tokens:比如原始序列为1-2-3-4,分解序列中采样一个为2-4-1-3,那么如果当前token为3,XLNet的方式就可以看到所有的信息,当然这也是理想情况。 +- 引入Transformer-XL的segment recurrence mechanism和relative encoding scheme。 +- 引入Masked Two-Stream Self-Attention解决PLM出现的目标预测歧义【the ambiguity in target prediction】问题。举个例子,比如分解序列中采样一个为2-4-6-1-3-5的序列,假设要预测[1]的token,按照经典的Transformer来计算next-token的概率分布,位置[1]token的概率就是通过[2,4,6]位置上的tokens来计算softmax,不会把[1]作为输入来计算的。但是如果以这种方式去预测next-token,这对[3,5]的预测就会产生影响,因为如果[1]的预测出现错误会把错误传给后面。对后面每一个token的预测,需要建立在之前token都已知的条件下。因此本文计算了两个self-attention计算方式,一个mask当前词,attention值记为$g$;一个已知当前词,attention值记为$h$。最后假设self-attention一共有$M$层,用第$M$层、$t$时刻的$g_t$,去预测词$x_t$。 + +### 2.2 【XLNet】动机 + +1. Bert 预训练和微调之间的不匹配 +2. Bert 的 Max Len 为 512 + +### 2.3 【XLNet】AR vs AE + +文章从AR(autoregressive,自回归)和AE(autoencoding,自编码)的角度出发,解释论文动机。 + +AR LM,即自回归语言模型。具体而言,给定一个序列,当前token时刻只知道前面的信息,而不知道后面的信息,即使分成正向和反向计算当前token时刻的概率分布,也是同样的原则,ELMo、GPT是属于这个范畴。对于一些自然语言理解任务而言,是给定上下文的,即使ELMo把两个的方向计算的信息concat,但也是独立计算,对上下文的编码是有缺陷的。 + +AE LM,即自编码语言模型。BERT通过预测原始数据里MASK掉的token来预训练语言模型,预测[MASK]使用了上下文信息,弥补了AR LM的缺陷。但是[MASK]只在预训练的时候用到,finetune的时候是不用的,这使得pretrain/train不一致。并且,BERT假定每个[MASK]与其他[MASK]是相互独立的,不能计算序列、长期依赖的联合概率。即使BERT的NSP预训练任务一定程度上给了模型建模句间关系的能力,但是还是对长文本不敏感。 + +### 2.4 【XLNet】 预训练 + +#### 2.4.1 【XLNet】Permutation Language Modeling + +首先代码会根据输入序列的长度采样一个排列,然后用Transformer中attention mask的方式实现排列,如果原始序列长度为T,那么理论上一共有T的阶乘种情况。PLM的目标函数就是所有排列情况(论文里设定:统共T种)的期望最大: + +![](img/20201026153603.png) + +这样pretrain和finetune阶段就一样了,输入都是原始序列,通过attention mask实现随机产生的排列。下图是排列语言模型的表现形式: + +![](img/20201026153725.png) + +> 注:假设要预测t=3的词,按照不同的排列顺序,h_3的上文都不一样,用attention-mask的方式得到t=3的上文。 + +#### 2.4.2 【XLNet】 Two-Stream Self-Attention for Target-Aware Representations + +##### 2.4.2.1 介绍 + +上面是构造输入,这里就是自回归地得到每一时刻的概率分布,示意图如下: + +![](img/20201026154010.png) + +##### 2.4.2.2 计算两个self-attention + +- (a) 代表context stream self-attention,以[1,t]时刻的词作为K、V,t时刻的词作为Q计算当前词的信息,把排列之后的原始序列信息用h记忆起来。 +- (b) 代表query stream self-attention,mask掉当前词,以[1,t-1]时刻的词作为K、V,t时刻的词作为Q预测当前词,得到概率分布。 +- (c) 代表通过多层的masked two-stream attention,最后用t时刻的$g_t$来预测x_t。 + +![](img/20201026154623.png) + +##### 2.4.2.3 Transformer-XL + +确定好目标函数之后,框架确定为Transformer-XL自回归语言模型。特点是relative positional encoding scheme和segment recurrence mechanism,更好地处理长文本,提升计算效率。具体不介绍了,详见参考文献[2]。 + +## 三、RoBERTa + +> 论文名称:《RoBERTa: A Robustly Optimized BERT Pretraining Approach》
+> 论文:https://arxiv.org/pdf/1907.11692.pdf
+> 代码:https://github.com/pytorch/fairseq
+ +### 3.1 【RoBERTa】动机 + +- Bert 序列模型的问题 +> 确定方法的哪些方面贡献最大可能是具有挑战性的; > -> 个人介绍:大佬们好,我叫杨夕,该项目主要是本人在研读顶会论文和复现经典论文过程中,所见、所思、所想、所闻,可能存在一些理解错误,希望大佬们多多指正。 - -![](img/微信截图_20210206164501.png) - -## 一、引言 - -- Bert 模型 之后,存在两个方向的优化: - - 第一个:性能方面优化->变大 (XLNet、RoBERTa、SpanBERT、MASS、UNILM、BART等) 【本章主要从该方面做介绍】 - - 第二个:工业界应用->变小 (ALBERT、Q-BERT、DistilBERT、FastBERT、TinyBERT等) - -## 二、Bert 变大篇 - -### 2.1 认识 XLNet 么?能不能讲一下? 和 Bert 的 区别在哪里? - -- AR vs AE - - AR:只利用到单向 context 的信息(前向或后向); - - AE(这里特指 BERT):Pre-training 阶段引入的 [MASK] 占位符在 Fine-tuning 阶段并不存在;同一个 sequence 如果有多个位置 [MASK],BERT 假定它们之间是独立的,这与事实不符 -- Bert 的问题: - - Bert 预训练和微调之间的不匹配 - - Bert 的 Max Len 为 512 -- XLNet 改进方法 - - 预训练: - - Permutation Language Modeling【解决Bert 预训练和微调之间的不匹配】 - - Two-Stream Self-Attention for Target-Aware Representations【解决PLM出现的目标预测歧义】 - - XLNet将最先进的自回归模型Transformer-XL的思想整合到预训练中【解决 Bert 的 Max Len 为 512】 - - 微调:直接利用 特定任务数据 微调 - -### 2.2 认识 RoBERTa 么?能不能讲一下? 和 Bert 的 区别在哪里? - -- 动机: - - 确定方法的哪些方面贡献最大可能是具有挑战性的 - - 训练在计算上是昂贵的的,限制了可能完成的调整量 -- 介绍:A Robustly Optimized BERT Pretraining Approach -- 思路: - - 预训练: - - 训练时间更长:更大规模的训练数据(16GB -> 160GB)、更大的 batch_size(256 -> 8K); - - 去掉下一句预测(NSP)任务 - - 去除 NSP 任务,输入格式相应修改为 FULL-SENTENCES; - - 输入粒度:由 character-level BPE 改为 byte-level BPE; - - masking 机制:由 static masking改为dynamic masking: - - static masking:仅在数据预处理阶段做一次随机 mask,每条数据在每个 epoch 中的 mask 方式不变; - - dynamic masking:每条数据在训练时随机mask,每个 epoch 中的 mask 方式不同。 - - 微调:直接利用 特定任务数据 微调 - -### 2.3 认识 SpanBERT 么?能不能讲一下? 和 Bert 的 区别在哪里? - -![](img/微信截图_20210120211554.png) - -- 思路: - - Span Masking:首先根据几何分布 l~Geo(p=0.2) 采样出 span 的长度(大于 10 则重新采样),然后根据均匀分布随机选择起始点,最后从起始点开始将 span 内的 token 进行 mask;注意,这个过程会进行多次,直到被 mask 的 token 数量达到阈值,如输入序列的 15%; - - Span Boundary Objective(SBO):对于 span 内的每一个 token,除了原始的 MLM 的 loss,再加 SBO 的 loss; -- Single-Sequence Training:去掉 NSP 任务,用一个长句替代原来的两个句子。 - -![](img/微信截图_20210120211800.png) -> Span Boundary Objective(SBO) - -### 2.4 认识 MASS 么?能不能讲一下? 和 Bert 的 区别在哪里? - -- 动机: - - BERT 在 NLU 任务上的成功,但是 NLG 面临着训练数据(paired data)匮乏的问题 - - 在大量 unpaired data 上做 pre-training 然后在少量 paired data 上做 fine-tuning,对 NLU 任务而言是同样有益的。然而,直接采用类似 BERT 的预训练结构(仅用 encoder 或 decoder)是不可取的,因为 NLG 任务通常是基于 encoder-decoder 的框架 - - GPT 或 Bert 只能单独对 encoder 或 decoder 进行 pre-training,MASS 可以对二者进行联合 pre-training -- 模型结构 - - 整体基于 Transformer,Encoder 被 mask 的 token 是连续的,Decoder 将 Encoder 中未被 mask 的 token 进行 mask,预测 Encoder 中被 mask 的 token。 - -![](img/微信截图_20210120212518.png) - -- 思路 - - Pre-training: - - 由于 NMT 涉及跨语言,因此采用了 4 种语言的数据,并在 Encoder、Decoder 的 input 中给每个 token 加上了一个 language embedding - - Encoder,mask 的 token 数量为序列长度的 50%,随机选择起点,mask 方式同 BERT(80% 替换为 [M],10%替换为其它随机 token,10% 不变) - - Decoder,移除被 mask 的 token,未 mask 的 token 依然保持它们的 positional encoding 不变 - - Fine-tuning: - - 同常规 Seq2Seq 任务 +> 训练在计算上是昂贵的的,限制了可能完成的调整量 +### 3.2 【RoBERTa】工作 +- 更大的模型参数量(论文提供的训练时间来看,模型使用 1024 块 V100 GPU 训练了 1 天的时间) +- 更大bacth size。RoBERTa 在训练过程中使用了更大的bacth size。尝试过从 256 到 8000 不等的bacth size。 +- 更多的训练数据(包括:CC-NEWS 等在内的 160GB 纯文本。而最初的BERT使用16GB BookCorpus数据集和英语维基百科进行训练) +另外,RoBERTa在训练方法上有以下改进: -## 参考 +### 3.3 【RoBERTa】预训练 + +#### 3.3.1 动态掩码 + +- 动态掩码。BERT 依赖随机掩码和预测 token。原版的 BERT 实现在数据预处理期间执行一次掩码,得到一个静态掩码。 而 RoBERTa 使用了动态掩码:每次向模型输入一个序列时都会生成新的掩码模式。这样,在大量数据不断输入的过程中,模型会逐渐适应不同的掩码策略,学习不同的语言表征。 + +#### 3.3.2 去掉NSP任务 + +- 动机:虽然是句子对输入,但其实一个句子不只有一句,而是文章里面的连续片段,可以包含多句。 +- 操作:采用去掉NSP而且一个样本是从同一个文档里面进行采样 + +#### 3.3.3 更大的数据量 + +在BERT采用的数据BOOKCORPUS + English WIKIPEDIA(共16G)基础上 + +- 增加 CC-NEWS(76GB) +- 增加 OPENWEBTEXT(38GB) +- 增加 STORIES(31GB) + +也就是RoBERTa一共用了160GB语料进行预训练。 + +#### 3.3.4 更大的batch_size + +BERT的batch_size是256,一共训练了1M步,实验证明,采用更大的batch_size以及训练更多步,可以提高性能。最后RoBERTa采用的batch_size是8K。 + +#### 3.3.5 Byte-level BPE编码 + +- BERT:采用的是基于character level的Byte-Pair Encoding(BPE)编码,词表大小是30K; +- RoBERTa:文本编码。Byte-Pair Encoding(BPE)是字符级和词级别表征的混合,支持处理自然语言语料库中的众多常见词汇。原版的 BERT 实现使用字符级别的 BPE 词汇,大小为 30K,是在利用启发式分词规则对输入进行预处理之后学得的。Facebook 研究者没有采用这种方式,而是考虑用更大的 byte 级别 BPE 词汇表来训练 BERT,这一词汇表包含 50K 的 subword 单元,且没有对输入作任何额外的预处理或分词。 + +## 四、ELECTRA + +> 论文名称:《ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generator》
+> 论文:https://arxiv.org/abs/2003.10555
+> 代码:https://github.com/google-research/electra
-1. XLNet: Generalized Autoregressive Pretraining for Language Understanding -2. RoBERTa: A Robustly Optimized BERT Pretraining Approach -3. SpanBERT: Improving Pre-training by Representing and Predicting Spans -4. MASS: Masked Sequence to Sequence Pre-training for Language Generation +### 4.1 【ELECTRA】动机 + +- Bert MLM 方式: 在训练Bert的时候,在输入上操作把15%的词语给替换成Mask,然后这其中有80%是Mask,有10%是替换成其他词语,最后剩下10%保持原来的词语。 +- 问题: 可以看到,Bert的训练中,每次相当于**只有15%的输入上是会有loss**的,而其他位置是没有的,这就导致了每一步的训练并没有被完全利用上,导致了训练速度慢。换句话说,就是模型只学习到 15%的 token 信息; + +### 4.2 【ELECTRA】介绍 + +提出了一种更有效的样本预训练任务,称为替换令牌检测。我们的方法不是掩盖输入,而是通过使用从 small generator network 采样的合理替代物替换一些令牌来破坏输入。然后,我们训练一个判别模型,该模型预测损坏的输入中的每个标记是否被生成器采样器代替,而不是训练一个预测损坏的令牌的原始身份的模型。 + +### 4.3 【ELECTRA】核心任务 + +- 核心:将生成式的Masked language model(MLM)预训练任务改成了判别式的Replaced token detection(RTD)任务,判断当前token是否被语言模型替换过; +- 思路:利用一个基于MLM的Generator来替换example中的某些个token,然后丢给Discriminator来判别 + +### 4.4 【ELECTRA】判别器 & 生成器 + +如下图所示,首先会训练一个生成器来生成假样本,然后Electra去判断每个token是不是被替换了。 + +![](img/20201015170627.png) + +### 4.5 【ELECTRA】算法流程 + +1. Generator G: + 1. 输入经过随机选择设置为[MASK]; + 2. 输入给 G,G 负责把[MASK]变成替换过的词; +2. Discriminator D: + 1. 预测 输入的句子 每个位置上的词语是否被替换过; + +> 注:
+> 1. Discriminator是训练完之后我们得到的预训练模型,Generator在训练完之后就没有用了 + +## 五、ERNIE 1.0 + +> 论文名称:《ERNIE: Enhanced Representation through Knowledge Integration》
+> 论文:https://arxiv.org/pdf/1904.09223.pdf
+> 代码:https://github.com/PaddlePaddle/ERNIE/tree/develop/ERNIE
+ +### 5.1 【ERNIE 1.0】动机 + +原生BERT是采用随机【MASK】会让模型不能充分学习到语义信息,降低学习难度。 + +![](img/微信截图_20210910210322.png) +> 注:Harry Potter的Harry被【MASK】掉,这时候让模型去预测被【MASK】掉的token,这种情况下,模型很可能是根据Potter从而预测Harry(毕竟Harry Potter在语料中共同出现频率的比较高),在这种情况下,模型也许并不是根据Harry Potter和J.K.Rowling的关系来预测出Harry的,换个角度,这样BERT学到的是规则,而并非语义信息。 + +### 5.2 【ERNIE 1.0】预训练 + +#### 5.2.1 Knowledge Integration + +针对 MASK 存在问题,ERNIE 1.0 将 MASK 分为三部分: + +- Basic-level Masking:与BERT一样; +- Entity-level Masking:把实体作为一个整体【MASK】,例如 J.K.Rowling 这个词作为一个实体,被一起【MASK】; +- Phrase-Level Masking:把短语作为一个整体【MASK】,如 a series of 作为一个短语整体,被一起【MASK】。 + +![](img/微信截图_20210910210729.png) + +#### 5.2.2 Dialogue Language Model(DLM) + +在 Bert 已有的预训练任务中,加入了 Dialogue Language Model 任务: + +- 作用:ERNIE 1.0 通过引入 多轮问答数据,使 ERNIE 学习到对话中的隐含关系,增加模型的语义表达能力。 +- 具体操作:把里面的单个token、实体、短语【MASK】掉,然后预测它们,另外在生成预训练数据的时候,有一定几率用另外的句子替代里面的问题和答案,所以模型还要预测是否是真实的问答对。 + +![](img/微信截图_20210910210907.png) + +> 注:注意看Segment Embedding被Dialogue Embedding代替了,但其它结构跟MLM模型是一样的,所以DLM任务可以和MLM任务联合训练,即Dialogue Embedding只会跟随DLM任务更新,Segment Embedding指挥跟随MLM任务跟新,而模型其它参数是随着DLM任务和MLM任务一起更新。 + +## 六、ERNIE 2.0 + +> 论文名称:《ERNIE 2.0: A Continual Pre-Training Framework for Language Understanding》
+> 论文:https://ojs.aaai.org//index.php/AAAI/article/view/6428
+> 代码:https://github.com/PaddlePaddle/ERNIE
+ +### 6.1 【ERNIE 2.0】动机 + +近两年,以 BERT、XLNet 为代表的无监督预训练技术在多个自然语言处理任务上取得了技术突破。基于大规模数据的无监督预训练技术在自然语言处理领域变得至关重要。 + +百度发现,之前的工作主要**通过词或句子的共现信号,构建语言模型任务进行模型预训练**。例如,**BERT 通过掩码语言模型和下一句预测任务进行预训练**。**XLNet 构建了全排列的语言模型,并通过自回归的方式进行预训练**。 + +然而,**除了语言共现信息之外,语料中还包含词法、语法、语义等更多有价值的信息**。例如,人名、地名、机构名等词语概念知识,句子间顺序和距离关系等结构知识,文本语义相似度和语言逻辑关系等语义知识。那么如果持续地学习各类任务,模型的效果能否进一步提升?这就是 ERNIE 2.0 希望探索的。 + +### 6.2 【ERNIE 2.0】架构 + +![](img/微信截图_20210910211607.png) + +ERNIE 2.0 中有一个很重要的概念便是连续学习(Continual Learning),**连续学习的目的是在一个模型中顺序训练多个不同的任务以便在学习下个任务当中可以记住前一个学习任务学习到的结果**。通过使用连续学习,可以不断积累新的知识,模型在新任务当中可以用历史任务学习到参数进行初始化,一般来说比直接开始新任务的学习会获得更好的效果 + +### 6.3 【ERNIE 2.0】预训练 + +#### 6.3.1 预训练连续学习 + +ERNIE 2.0 提出三种策略来让模型同时学习3个任务: + +- 策略一:**Multi-task Learning**,就是让模型同时学这3个任务,具体的让这3个任务的损失函数权重双加,然后一起反向传播更新参数; +- 策略二:**Continual Learning**,先训练任务1,再训练任务2,再训练任务3,这种策略的缺点是容易遗忘前面任务的训练结果,最后训练出的模型容易对最后一个任务过拟合; +- 策略三:**Sequential Multi-task Learning**,连续多任务学习,即第一轮的时候,先训练任务1,但不完全让它收敛训练完,第二轮,一起训练任务1和任务2,同样不让模型收敛完,第三轮,一起训练三个任务,直到模型收敛完。 + +![](img/微信截图_20210910212046.png) + +论文采用策略三的思想。具体的,如下图所示,每个任务有独立的损失函数,句子级别的任务可以和词级别的任务一起训练,相信做过联合训练的同学并不陌生。 + +![](img/微信截图_20210910212205.png) + +#### 6.3.2 更多的无监督预训练任务 + +由于是多任务学习,模型输入的时候额外多了一个Task embedding。 + +![](img/微信截图_20210910212307.png) + +具体的三种类型的无监督训练任务是哪三种呢?每种里面又包括什么任务呢? + +1. 任务一:词法级别预训练任务 + +- Knowledge Masking Task:这任务同ERNIE 1.0一样,把一些字、短语、实体【MASK】掉,预测【MASK】词语; +- Capitalization Prediction Task:预测单词是大写还是小写; +- Token-Document Relation Prediction Task:预测在段落A中出现的token,是否在文档的段落B中出现。 + +2. 任务二:语言结构级别预训练任务 + +- Sentence Reordering Task:把文档中的句子打乱,预测正确顺序; +- Sentence Distance Task:分类句子间的距离(0:相连的句子,1:同一文档中不相连的句子,2:两篇文档间的句子)。 + +1. 任务三:语句级别预训练任务 + +- Discourse Relation Task:计算两句间的语义和修辞关系; +- IR Relevance Task:短文本信息检索关系,搜索数据(0:搜索并点击,1:搜素并展现,2:无关)。 + +## 七、ERNIE-T + +> 论文名称:《ERNIE: Enhanced Language Representation with Informative Entities》
+> 论文:https://arxiv.org/pdf/1905.07129.pdf
+> 代码:https://github.com/thunlp/ERNIE
+ +### 7.1 【ERNIE-T】动机 + +例如下面的句子,我们的任务目标是对“Bob Dylan”进行实体类别识别(Entity Typing)。所谓Entity Typing,是指给定一个实体指代(entity mention),根据实体的上下文信息来对实体的具体语义类别进行预测。例如在示例1中,Bob Dylan的entity typing的值为“songwriter”和“writer”,因为“Blowin' in the wind”是一首歌,“Chronicies:Volume One”是一本书。对于BERT来说,仅仅通过上下文是很难预测出正确的结果的,因为它没有关于这两个作品类别的信息。 + +但是如果我们引入了实体额外的知识图谱呢,如图1。根据图谱提供的信息,我们知道了Blowin' in the wind”是一首歌以及“Chronicies:Volume One”是一本书,那么我们再对“Boby Dylan”进行实体类别识别时就容易多了。 + +![](img/微信截图_20210910213336.png) +> 示例1: Bob Dylan wrote Blowin' in the Wind in 1962, and wrote Chronicies: Volume One in 2004. + +### 6.2 【ERNIE-T】架构 + +![](img/微信截图_20210910213420.png) + +### 6.3 【ERNIE-T】预训练 + +#### 6.3.1 引入知识图谱 + +ERNIE-T 引入知识图谱来增强预训练模型的语义表达能力,其实预训练时就是在原来bert的基础上增加了一个实体对齐的任务。 + +#### 6.3.2 异构信息融合 + +因为词向量和实体的知识图谱是两个异构的信息,那么如何设计一个网络来融合这两个信息则是我们需要解决的首要问题。如图2所示,ERNIE-T由两个模块组成,它们是底层文本编码器(underlying textual encoder,T-Encoder)和上层知识编码器(upper knowledge encoder,K-Encoder)。 + +#### 6.3.3 dAE + +提出了随机mask tokens-entity中的entity,然后去预测该位置对应的entity,本质上和MLM(mask language model)任务一致,都属于去噪自编码。具体mask的细节: + +1. 5%的tokens-entity对采用随机用其他的entity来替换,这主要是引入噪声,因为在实际的任务中也存在这种情况。 +2. 15%的tokens-entity对采用随机maskentity,然后来预测这个entity。 +3. 80%保持正常。 + +这篇论文主要的工作就是增加了这个任务,另外也提出了在实体类型和关系抽取两个任务上新的预训练方式,具体如下图: + +![](img/微信截图_20210910213837.png) + +就是引入了一些特殊的token来表明另外一些特殊token的身份。因为引入了实体对齐任务,因此该模型在一些和知识图谱相关的下游任务上要优于bert。 + +## 参考 +1. [《BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding》](https://arxiv.org/pdf/1810.04805.pdf) +2. [《XLNet: Generalized Autoregressive Pretraining for Language Understanding》](https://arxiv.org/abs/1906.08237) +3. [《RoBERTa: A Robustly Optimized BERT Pretraining Approach》](https://arxiv.org/pdf/1907.11692.pdf) +4. [《ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generator》](https://arxiv.org/abs/2003.10555) +5. [《ERNIE: Enhanced Representation through Knowledge Integration》](https://arxiv.org/pdf/1904.09223.pdf) +6. [《ERNIE 2.0: A Continual Pre-Training Framework for Language Understanding》](https://ojs.aaai.org//index.php/AAAI/article/view/6428) +7. 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zx+(7n+?D4IrhvI(MnuQ?ESf{JR*gP1Vndfx`xKYjTyK&=bRy}mQ~s(yCXr+&o|f2& zBb6%!(TGO;ny8*7cOkcV9P(qXzd7;GH3I-+-G+S~$(3nz1}$HA(UJ!zE>mf~fWvS# zdx6UTy{IzTgrs*;K-6A*N^Q7VGRa5^Y6||jzfyY`OZA_$MI}V?Gk6r+__bOEb!P11 zQ{wDCqIVKn#YwO`O!{}o2Z(PZIt(Y`1wP&>Tb}sOtn7o4|NWe3*Yp22YL>{(_WIu^ o)JP=f`qxYUzu)fF6!M02z;A*2qhHD2X~26LYOht{%I|{z55R literal 0 HcmV?d00001 diff --git a/NLPinterview/ner/crf/readme.md b/NLPinterview/ner/crf/readme.md index 96f4a5c..610d840 100644 --- a/NLPinterview/ner/crf/readme.md +++ b/NLPinterview/ner/crf/readme.md @@ -70,13 +70,13 @@ $$ #### 3.1.4 隐马尔科夫算法 中 两个假设 是什么? -- 假设一:输出观测值间严格独立,仅取决于对应隐状态。假设隐状态$x_i$ 的状态满足马尔可夫过程,i时刻的状态$x_i$ 的条件分布,仅与其前一个状态$x_{i-1}$相关,即: +- 齐次马尔可夫性假设:即假设隐藏的马尔科夫链在任意时刻 t 的状态只依赖于其前一时刻的状态,与其他时刻的状态及观测无关,也与时刻 t 无关; $$ P(x_i|x_1,x_2,...,x_{i-1}) = P(x_i|x_{i-1}) $$ -- 假设二:状态转移过程中,当前状态只与前一状态相关。假设观测序列中各个状态仅取决于它所对应的隐状态,即: +- 观测独立性假设:即假设任意时刻的观测只依赖于该时刻的马尔科夫链的状态,与其他观测及状态无关。 $$ P(y_i|x_1,x_2,...,x_{i-1},y_1,y_2,...,y_{i-1},y_{i+1},...) = P(y_i|x_{i}) @@ -96,18 +96,40 @@ $$ ### 3.2 隐马尔科夫算法 模型计算过程篇 -#### 3.2.1 隐马尔科夫算法 学习训练过程 是什么样的? +#### 3.2.1 隐马尔科夫算法 序列概率计算过程 是什么样的? + +1. 思想 + +如何对一条序列计算其整体的概率。即目标是计算出 $P(O | \lambda)$ ; + +给定模型 $\lambda=(A,B,π)$ 和观测序列 O=(o1,o2,...,oT) ,计算在模型 $\lambda$ 下观测序列 $O$ 出现的概率 $P(O|\lambda)$ + +1. 常用方法 + +- 直接计算法(穷举搜索) + +由于有隐藏的状态序列 I 的存在,我们是无法计算 $P(O|\lambda)$ 的。一种常见的做法是把 I 边缘掉,即 $P(O|\lambda)=\sum(P(O,I|\lambda))$ ,当然,这种计算复杂度非常高,为 $O(TN^2)$ + +- 前向算法 + +减少计算量的原因在于每一次计算直接引用前一个时刻的计算结果,避免重复计算,计算复杂度将为$O(T^2 * N)$ + +- 后向算法 + +#### 3.2.2 隐马尔科夫算法 学习训练过程 是什么样的? 1. 思想 找出数据的分布情况,也就是模型参数的确定; + 已知观测序列 O=(o1,o2,...,oT) ,估计模型 $\lambda=(A,B,π)$ 参数,使得在该模型下观测序列概率 $P(O|\lambda)$ 最大,即用极大似然估计的方法估计参数 + 2. 常用方法 - 极大似然估计:该算法在训练数据是**会 将 观测状态序列 $O$ 和 隐状态序列 $I$**; - Baum-Welch(前向后向):该算法在训练数据是**只会 将 观测状态序列 $O$**; -#### 3.2.2 隐马尔科夫算法 序列标注(解码)过程 是什么样的? +#### 3.2.3 隐马尔科夫算法 序列标注(解码)过程 是什么样的? 1. 思想 @@ -121,19 +143,13 @@ Viterbi计算有向无环图的一条最大路径: ![](img/viterbi.png) -#### 3.2.3 隐马尔科夫算法 序列概率过程 是什么样的? - -1. 思想 +### 3.3 HMM模型三个基本问题的联系? -如何对一条序列计算其整体的概率。即目标是计算出 $P(O | \lambda)$ ; +三个基本问题 存在 渐进关系。首先,要学会用前向算法和后向算法算观测序列出现的概率,然后用Baum-Welch算法求参数的时候,某些步骤是需要用到前向算法和后向算法的,计算得到参数后,我们就可以用来做预测了。因此可以看到,三个基本问题,它们是渐进的,对于做NLP的同学来说,应用HMM模型做解码任务应该是最终的目的。 -2. 常用方法 +### 3.4 隐马尔科夫算法 问题篇 -- 直接计算法(穷举搜索) -- 前向算法 -- 后向算法 - -### 3.3 隐马尔科夫算法 问题篇 +因为HMM模型其实它简化了很多问题,做了某些很强的假设,如**齐次马尔可夫性假设**和**观测独立性假设**,做了假设的好处是,**简化求解的难度**,坏处是**对真实情况的建模能力变弱**了。 在序列标注问题中,隐状态(标注)不仅和单个观测状态相关,还和观察序列的长度、上下文等信息相关。例如词性标注问题中,一个词被标注为动词还是名词,不仅与它本身以及它前一个词的标注有关,还依赖于上下文中的其他词。 @@ -217,7 +233,7 @@ MEMM 容易出现标注偏置问题,MEMM倾向于选择拥有更少转移的 #### 5.2.1 什么是 CRF? -设X与Y是随机变量,P(Y|X)是给定条件X的条件下Y的条件概率分布,若随机变量Y构成一个由无向图G=(V,E)表示的马尔科夫随机场。则称条件概率分布P(X|Y)为条件随机场。 +设 X 与 Y 是随机变量,**P(Y|X) 是给定条件 X 的条件下 Y 的条件概率分布**,若**随机变量 Y 构成一个由无向图G=(V,E)表示的马尔科夫随机场**。则称 **条件概率分布P(X|Y)为条件随机场**。 ![条件随机场图片](img/条件随机场.png) @@ -240,7 +256,12 @@ $$ ![条件随机场图片](img/条件随机场.png) -- 参数化形式 +#### 5.2.4 CRF 的 三个基本问题 是什么? + +##### 5.2.4.1 概率计算问题 + +- 定义:给定 观测序列 x 和 状态序列 y, 计算概率 P(y|x) +- 公式定义: $$ p\left(y | x\right)=\frac{1}{Z\left(x\right)} \prod_{i=1}^{n} \exp \left(\sum_{i, k} \lambda_{k} t_{k}\left(y_{i-1}, y_{i}, x, i\right)+\sum_{i, l} \mu_{l} s_{l}\left(y_{i}, x, i\right)\right) @@ -258,7 +279,24 @@ $$ > $s_1$ 为定义在节点上的特征函数,状态特征,依赖于当前位置。 -#### 5.2.4 CRF 的 流程是什么? +- 解决方法:前向计算、后向计算 + +##### 5.2.4.2 学习计算问题 + +- 定义:给定训练数据集估计条件随机场模型参数的问题,即条件随机场的学习问题。 +- 公式定义:利用极大似然的方法来定义我们的目标函数 + +![](IMG/微信截图_20210901095003.png) + +- 解决方法:随机梯度法、牛顿法、拟牛顿法、迭代尺度法这些优化方法来求解得到参数 +- 目标:解耦 模型定义,目标函数,优化方法 + +##### 5.2.4.3 预测问题 + +- 定义:给定条件随机场 P(Y|X) 和输入序列(观测序列) x ,求条件概率最大的输出序列(标记序列) y* ,即对观测序列进行标注。 +- 方法:维特比算法 + +#### 5.2.5 CRF 的 流程是什么? 1. 选择特征模板:抽取文本中的字符组合 or 具有其他特殊意义的标记组成特征,作为当前 token 在模板中的表示; 2. 构建特征函数:通过一组函数来完成由特征向数值转换的过程,使特征与权重对应; @@ -379,14 +417,18 @@ $$ ## 六、对比篇 -### 6.1 CRF模型 和 HMM和MEMM模型 区别? +### 6.1 CRF模型 和 HMM 和 MEMM 模型 区别? - 相同点:MEMM、HMM、CRF 都常用于 序列标注任务; - 不同点: - - 与 HMM 的区别:CRF 能够解决 HMMM 因其输出独立性假设,导致其不能考虑上下文的特征,限制了特征的选择的问题; - - 与 MEMM 的区别:MEMM 虽然能够解决 HMM 的问题,但是 MEMM 由于在每一节点都要进行归一化,所以只能找到局部的最优值,同时也带来了标记偏见的问题,即凡是训练语料中未出现的情况全都忽略掉。 - - CRF :很好的解决了这一问题,他并不在每一个节点进行归一化,而是所有特征进行全局归一化,因此可以求得全局的最优值。 + - 与 HMM 的区别:CRF 能够解决 HMMM 因其输出独立性假设,导致其不能考虑上下文的特征,限制了特征的选择的问题; + - 与 MEMM 的区别:MEMM 虽然能够解决 HMM 的问题,但是 MEMM 由于在每一节点都要进行归一化,所以只能找到局部的最优值,同时也带来了标记偏见的问题,即凡是训练语料中未出现的情况全都忽略掉。 + - CRF :很好的解决了这一问题,他并不在每一个节点进行归一化,而是所有特征进行全局归一化,因此可以求得全局的最优值。 + +### 6.2 为什么 CRF模型 会比 HMM 被普遍使用? +- 原因 1:CRF模型 属于 判别式模型,在 序列标注 任务上,效果优于 生成式模型; +- 原因 2:HMM 提出 齐次马尔可夫性假设 和 观测独立性假设,这两个假设过强,而 CRF 只需要满足 局部马尔可夫性就好,通过降低假设的方式,提升模型效果; ## 参考资料 @@ -397,4 +439,6 @@ $$ 4. [如何轻松愉快地理解条件随机场(CRF)?](https://www.jianshu.com/p/55755fc649b1) 5. [概率图模型体系:HMM、MEMM、CRF](https://zhuanlan.zhihu.com/p/33397147) 6. [CRF 视频介绍](https://b23.tv/BV19t411R7QU/p1) +7. [概率图模型(二):捋一捋HMM模型](https://zhuanlan.zhihu.com/p/398880896) +8. [概率图模型(三):理一理CRF模型](https://zhuanlan.zhihu.com/p/402132237) diff --git a/README.md b/README.md index b674897..d14dfbb 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ # 【关于 NLP】百问百答 -> 作者:杨夕、芙蕖、李玲、陈海顺、twilight、LeoLRH、JimmyDU、艾春辉、张永泰、金金金 +> 作者:杨夕、芙蕖、李玲、陈海顺、twilight、LeoLRH、JimmyDU、张永泰 > > 面筋地址:https://github.com/km1994/NLP-Interview-Notes >
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