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提高自然语言处理模型鲁棒性的秘密武器——对抗训练

训练 处理 模型 提高 自然语言 对抗 秘密武器
2023-09-27 14:29:07 时间
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提高自然语言处理模型鲁棒性的秘密武器——对抗训练

自然语言处理(Natural Language Processing, NLP)领域是人工智能的热门研究领域之一，它研究如何使用计算机处理自然语言的各种任务，包括文本分类、命名实体识别、语义分析、情感分析等等。随着NLP的发展，对抗训练(Adversarial Training)成为了一个备受关注的研究方向，它通过对抗生成样本的方式，来提高模型的鲁棒性和泛化性能，从而提高NLP模型在实际场景中的可用性和可靠性。

本文将详细介绍自然语言处理领域的对抗训练，包括其原理、应用以及代码实现等方面的内容，帮助读者深入了解这个研究方向。

1. 什么是对抗训练？

对抗训练是一种在深度学习中使用的技术，其主要思想是通过生成对抗样本来训练模型。这些对抗样本是由原始样本经过人为干扰后生成的，干扰的方式可以是添加噪声、修改输入等等。对抗训练的目的是提高模型的鲁棒性和泛化性能，从而使其在实际应用场景中更加可靠。

对抗训练最早是在图像领域中被提出的，后来被逐渐应用到自然语言处理领域。在自然语言处理领域中，对抗样本的生成方式可以是针对文本添加噪声，或者对文本进行修改。

在对抗训练中，模型会同时接收原始样本和对抗样本进行训练。对于每个训练步骤，模型会根据原始样本和对抗样本计算损失函数，然后通过反向传播算法更新模型参数。这样，模型就可以学习到如何区分原始样本和对抗样本，并且提高鲁棒性和泛化性能。

2. 对抗训练的原理

对抗训练的原理可以用以下几个步骤来描述：

  • 随机生成对抗样本。对抗样本是通过对原始样本进行人为干扰而生成的。在自然语言处理领域中，可以通过添加噪声、修改输入、打乱顺序等方式来生成对抗样本。
  • 将对抗样本和原始样本输入模型进行训练。在每个训练步骤中，模型会同时接收原始样本和对抗样本进行训练，并计算它们的损失函数。损失函数是用来衡量模型预测结果与真实结果之间的差距，它通常使用交叉熵(Cross-Entropy)损失函数。
  • 更新模型参数。对抗训练中，模型参数的更新通常使用反向传播算法。反向传播算法会根据损失函数的梯度更新模型参数，以便使模型更好地区分原始样本和对抗样本。

在对抗训练中，对抗样本的生成是一个关键的步骤。对于文本数据，常见的生成方法包括：

  • 添加噪声：在文本中添加噪声，例如替换单词、删除单词、插入单词、打乱单词顺序等等。
  • 改变输入：改变文本的输入，例如修改单词的拼写、改变单词的顺序、更改单词的语义等等。
  • 对抗训练生成器：通过对抗训练生成器来生成对抗样本。对抗训练生成器通常是一个对抗生成网络(Adversarial Generation Network, AGN)，它通过生成对抗样本来欺骗模型。

对抗训练的核心思想是让模型在训练中学习到如何区分原始样本和对抗样本。因此，在训练过程中，对抗样本的生成需要考虑两个方面：

  • 对抗样本与原始样本的相似性：对抗样本应该越接近原始样本，这样才能保证模型学习到对应的特征。
  • 对抗样本的干扰程度：对抗样本需要引入足够的干扰，使其足以欺骗模型，从而使模型能够更好地学习到抵御攻击的能力。

3. 对抗训练的应用

对抗训练已经被广泛应用于自然语言处理领域，包括文本分类、命名实体识别、机器翻译、情感分析等等。在下面的几个应用场景中，我们将介绍对抗训练在自然语言处理中的应用。

3.1 文本分类

文本分类是自然语言处理中的一个重要任务，它通常使用深度学习模型进行分类。在文本分类任务中，对抗训练可以帮助模型提高鲁棒性和泛化能力，从而提高模型在实际应用场景中的可靠性。

例如，可以使用对抗训练来提高模型对文本中的噪声和干扰的容忍程度。对抗样本可以通过对原始文本进行添加噪声、更改单词顺序、替换单词等方式生成。通过对抗训练，模型可以学习到如何区分原始样本和对抗样本，从而提高鲁棒性和泛化能力。

以下是一个文本分类的对抗训练代码示例，使用的是PyTorch深度学习框架。

import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
from sklearn.datasets import fetch_20newsgroups
from torchtext.legacy import data
from torchtext.legacy.datasets import IMDB
from torchtext.legacy.data import Field, LabelField, TabularDataset, BucketIterator
import random

# 加载IMDB数据集
TEXT = Field(sequential=True, tokenize='spacy', batch_first=True, lower=True)
LABEL = LabelField(dtype=torch.float, batch_first=True)
train_data, test_data = IMDB.splits(TEXT, LABEL)

# 定义模型
class CNN_Text(nn.Module):
    def __init__(self):
        super(CNN_Text, self).__init__()
        self.conv1 = nn.Conv2d(1, 32, (3, 100), padding=(1, 0))
        self.conv2 = nn.Conv2d(32, 64, (3, 100), padding=(1, 0))
        self.conv3 = nn.Conv2d(64, 128, (3, 100), padding=(1, 0))
        self.dropout = nn.Dropout(0.5)
        self.fc1 = nn.Linear(128 * 4 * 4, 100)
        self.fc2 = nn.Linear(100, 1)

    def forward(self, x):
        x = x.unsqueeze(1)
        x = F.relu(self.conv1(x))
        x = F.relu(self.conv2(x))
        x = F.relu(self.conv3(x))
        x = F.max_pool2d(x, (2, 2))
        x = x.view(-1, 128 * 4 * 4)
        x = self.dropout(x)
        x = F.relu(self.fc1(x))
        x = self.dropout(x)
        x = self.fc2(x)
        return x

# 定义训练函数
def train(model, iterator, optimizer, criterion, device):
    model.train()
    epoch_loss = 0
    for i, batch in enumerate(iterator):
        text = batch.text
        label = batch.label.float()
        text = text.to(device)
        label = label.to(device)
        optimizer.zero_grad()
        output = model(text).squeeze(1)
        loss = criterion(output, label)
        loss.backward()
        optimizer.step()
        epoch_loss += loss.item()
    return epoch_loss / len(iterator)

# 定义测试函数
def evaluate(model, iterator, criterion, device):
    model.eval()
    epoch_loss = 0
    with torch.no_grad():
        for i, batch in enumerate(iterator):
            text = batch.text
            label = batch.label.float()
            text = text.to(device)
            label = label.to(device)
            output = model(text).squeeze(1)
            loss = criterion(output, label)
            epoch_loss += loss.item()
    return epoch_loss / len(iterator)

# 定义对抗样本生成函数
def generate_adversarial_example(model, text, label, criterion, epsilon):
	model.eval()
	text.requires_grad = True
	output = model(text)
	loss = criterion(output, label)
	loss.backward()
	text_grad = text.grad.data
	text_adv = torch.clamp(text + epsilon * torch.sign(text_grad), min=0, max=1)
	return text_adv

# 设置训练参数
batch_size = 64
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
train_iterator, test_iterator = BucketIterator.splits((train_data, test_data), batch_size=batch_size, device=device)

model = CNN_Text().to(device)
optimizer = optim.Adam(model.parameters(), lr=0.001)
criterion = nn.BCEWithLogitsLoss().to(device)

# 对抗训练循环
num_epochs = 10
epsilon = 0.1
for epoch in range(num_epochs):
	train_loss = train(model, train_iterator, optimizer, criterion, device)
	test_loss = evaluate(model, test_iterator, criterion, device)
	print(f'Epoch: {epoch+1} Train Loss: {train_loss:.3f} Test Loss: {test_loss:.3f}')
	for batch in train_iterator:
		text = batch.text
		label = batch.label.float()
		text_adv = generate_adversarial_example(model, text, label, criterion, epsilon)
		output_adv = model(text_adv)
		loss_adv = criterion(output_adv, label)
		loss_adv.backward()
		optimizer.step()

上述代码中，我们首先定义了一个基于卷积神经网络的文本分类模型CNN_Text。接着定义了训练函数和测试函数，使用Adam优化器和BCEWithLogitsLoss损失函数进行训练。在对抗训练循环中，我们使用generate_adversarial_example函数生成对抗样本，然后对模型进行训练。

3.2 命名实体识别

命名实体识别(Named Entity Recognition, NER)是自然语言处理中的一个重要任务，它通常用于识别文本中的人名、地名、组织名等实体。

在命名实体识别任务中，对抗训练可以提高模型的鲁棒性和泛化能力，从而使其能够更好地识别噪声和干扰。

以下是一个命名实体识别的对抗训练代码示例，使用的是PyTorch深度学习框架。

import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
import random
import spacy
from spacy.util import minibatch, compounding
from spacy import displacy
from sklearn.metrics import confusion_matrix, classification_report

# 加载数据集
nlp = spacy.load('en_core_web_sm')
train_data = [("John lives in New York City.", {"entities": [(0, 4, "PERSON"), (14, 28, "GPE")]}),
("I like to eat pizza in Chicago.", {"entities": [(23, 30, "GPE")]}),
("Steve Jobs was the CEO of Apple.", {"entities": [(0,10, "PERSON"), (26, 31, "ORG")]}),
("The Eiffel Tower is located in Paris.", {"entities": [(4, 16, "LOC"), (31, 36, "LOC")]}),
("I am a student at the University of California, Berkeley.", {"entities": [(21, 50, "ORG"), (54, 63, "GPE")]}),
("President Obama visited India in 2015.", {"entities": [(0, 15, "PERSON"), (23, 28, "GPE"), (32, 36, "DATE")]}),
("Albert Einstein was a famous scientist.", {"entities": [(0, 15, "PERSON")]}),
("The Mona Lisa was painted by Leonardo da Vinci.", {"entities": [(4, 13, "PERSON"), (29, 44, "PERSON")]}),
("Mount Everest is the highest peak in the world.", {"entities": [(0, 12, "LOC")]}),
("I live in San Francisco.", {"entities": [(10, 23, "GPE")]}),
("Harry Potter is a book written by J.K. Rowling.", {"entities": [(0, 12, "PERSON"), (35, 46, "PERSON")]}),
("The Great Wall is a landmark in China.", {"entities": [(4, 15, "LOC"), (27, 32, "GPE")]}),
("Beyonce is a famous singer.", {"entities": [(0, 7, "PERSON")]}),
("The Statue of Liberty is located in New York City.", {"entities": [(4, 21, "LOC"), (38, 52, "GPE")]}),
("I am going to London next week.", {"entities": [(16, 22, "LOC"), (23, 31, "DATE")]}),
("The Titanic sank in 1912.", {"entities": [(4, 11, "PRODUCT"), (15, 19, "DATE")]}),
("Barack Obama was the President of the United States.", {"entities": [(0, 12, "PERSON"), (26, 46, "ORG"), (50, 67, "GPE")]}),
("I study at Stanford University.", {"entities": [(15, 34, "ORG")]}),
("The Amazon River flows through South America.", {"entities": [(4, 18, "LOC"), (27, 38, "LOC")]}),("I like to eat sushi in Tokyo.", {"entities": [(23, 28, "GPE")]}),
("Elon Musk is the CEO of Tesla.", {"entities": [(0, 9, "PERSON"), (23, 28, "ORG")]}),
("The sun rises in the east and sets in the west.", {"entities": []})]

# 定义模型
class NER_Model(nn.Module):
	def init(self, num_labels):
		super(NER_Model, self).init()
		self.num_labels = num_labels
		self.hidden_dim = 128
		self.embedding_dim = 300
		self.dropout_prob = 0.5
		self.bilstm = nn.LSTM(self.embedding_dim, self.hidden_dim, num_layers=2, bidirectional=True, batch_first=True, dropout=self.dropout_prob)
		self.fc = nn.Linear(self.hidden_dim*2, self.num_labels)
		self.dropout = nn.Dropout(self.dropout_prob)
	def forward(self, x):
	    out, _ = self.bilstm(x)
	    out = self.fc(out)
	    return out
# 定义训练函数
def train(model, optimizer, criterion, train_data, num_epochs, device):
	for epoch in range(num_epochs):
		random.shuffle(train_data)
		losses = []
		batches = minibatch(train_data, size=compounding(4.0, 32.0, 1.001))
		for batch in batches:
			texts, annotations = zip(*batch)
			targets, _ = zip(*annotations)
			targets = [torch.tensor(t).to(device) for t in targets]
			optimizer.zero_grad()
			texts = [nlp.tokenizer(text) for text in texts]
			texts = [torch.tensor(text.vector).unsqueeze(0).to(device) for text in texts]
			texts = torch.cat(texts, dim=0)
			targets = torch.cat(targets, dim=0)
			output = model(texts)
			loss = criterion(output.view(-1, output.shape[-1]), targets.view(-1))
			loss.backward()
			optimizer.step()
			losses.append(loss.item())
			print('Epoch: {} Loss: {}'.format(epoch+1, np.mean(losses)))

# 定义测试函数
def evaluate(model, test_data, device):
	true_labels = []
	predicted_labels = []
	with torch.no_grad():
		for text, annotation in test_data:
			true_labels.append(annotation['entities'])
			text = nlp.tokenizer(text)
			text = torch.tensor(text.vector).unsqueeze(0).to(device)
			output = model(text)
			predicted = torch.argmax(output, dim=-1)
			predicted_labels.append(predicted.cpu().numpy().tolist())
			true_labels = [label for labels in true_labels for label in labels]
			predicted_labels = [label for labels in predicted_labels for label in labels]
			print(classification_report(true_labels, predicted_labels))
# 设置训练参数
num_epochs = 10
batch_size = 32
num_labels = nlp.entity.move_names[1:]
optimizer = optim.Adam(model.parameters(), lr=0.001)
criterion = nn.CrossEntropyLoss().to(device)
model = NER_Model(len(num_labels)).to(device)

# 对抗训练循环
epsilon = 0.1
for epoch in range(num_epochs):
	train_loss = train(model, optimizer, criterion, train_data, num_epochs, device)
	evaluate(model, test_data, device)
	for text, annotation in train_data:
		text = nlp.tokenizer(text)
		text = torch.tensor(text.vector).unsqueeze(0).to(device)
		targets, _ = zip(*annotation['entities'])
		targets = [torch.tensor(t).to(device) for t in targets]
		text_adv = generate_adversarial_example(model, text, targets, criterion, epsilon)
		output_adv = model(text_adv)
		targets = torch.cat(targets, dim=0)
		loss_adv = criterion(output_adv.view(-1, output_adv.shape[-1]), targets.view(-1))
		loss_adv.backward()
		optimizer.step()

上述代码中，我们首先定义了一个基于双向LSTM的命名实体识别模型NER_Model。接着定义了训练函数和测试函数，使用Adam优化器和CrossEntropyLoss损失函数进行训练。在对抗训练循环中，我们使用generate_adversarial_example函数生成对抗样本，然后对模型进行训练。

3.3 机器翻译

机器翻译(Machine Translation, MT将一种自然语言转换为另一种自然语言的过程。在机器翻译任务中，对抗训练可以提高模型对噪声和干扰的鲁棒性，从而使其能够更好地处理不准确或含有错误的翻译。

以下是一个机器翻译的对抗训练代码示例，使用的是PyTorch深度学习框架。

import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
from torchtext.legacy.datasets import Multi30k
from torchtext.legacy.data import Field, BucketIterator
import random

# 加载数据集
SRC = Field(tokenize='spacy', init_token='<sos>', eos_token='<eos>', lower=True, batch_first=True)
TRG = Field(tokenize='spacy', init_token='<sos>', eos_token='<eos>', lower=True, batch_first=True)
train_data, valid_data, test_data = Multi30k.splits(exts=('.de', '.en'), fields=(SRC, TRG))

# 构建词汇表
SRC.build_vocab(train_data, min_freq=2)
TRG.build_vocab(train_data, min_freq=2)

# 定义模型
class Encoder(nn.Module):
    def __init__(self, input_dim, emb_dim, enc_hid_dim, dec_hid_dim, dropout):
        super().__init__()

        self.embedding = nn.Embedding(input_dim, emb_dim)
        self.rnn = nn.GRU(emb_dim, enc_hid_dim, bidirectional=True, batch_first=True)
        self.fc = nn.Linear(enc_hid_dim * 2, dec_hid_dim)
        self.dropout = nn.Dropout(dropout)

    def forward(self, src):
        embedded = self.dropout(self.embedding(src))
        outputs, hidden = self.rnn(embedded)
        hidden = torch.cat((hidden[-2,:,:], hidden[-1,:,:]), dim=1)
        hidden = torch.tanh(self.fc(hidden))
        return outputs, hidden

class Attention(nn.Module):
    def __init__(self, enc_hid_dim, dec_hid_dim):
        super().__init__()

        self.attn = nn.Linear((enc_hid_dim * 2) + dec_hid_dim, dec_hid_dim)
        self.v = nn.Linear(dec_hid_dim, 1, bias=False)

    def forward(self, hidden, encoder_outputs):
        src_len = encoder_outputs.shape[1]
        hidden = hidden.unsqueeze(1).repeat(1, src_len, 1)
        energy = torch.tanh(self.attn(torch.cat((hidden, encoder_outputs), dim=2)))
        attention = self.v(energy).squeeze(2)
        return torch.softmax(attention, dim=1)

class Decoder(nn.Module):
    def __init__(self, output_dim, emb_dim, enc_hid_dim, dec_hid_dim, dropout, attention):
        super().__init__()

        self.output_dim = output_dim
        self.attention = attention
        self.embedding = nn.Embedding(output_dim, emb_dim)
        self.rnn = nn.GRU((enc_hid_dim * 2) + emb_dim, dec_hid_dim, batch_first=True)
        self.fc_out = nn.Linear((enc_hid_dim * 2) + dec_hid_dim + emb_dim, output_dim)
        self.dropout = nn.Dropout(dropout)

    def forward(self, input, hidden, encoder_outputs):
        input = input.unsqueeze(1)
        embedded = self.dropout(self.embedding(input))
        a = self.attention(hidden, encoder_outputs)
	    a = a.unsqueeze(1)
	    encoder_outputs = encoder_outputs.transpose(1, 2)
	    weighted = torch.bmm(a, encoder_outputs).transpose(1, 2)
	    rnn_input = torch.cat((embedded, weighted), dim=2)
	    output, hidden = self.rnn(rnn_input, hidden.unsqueeze(0))
	    embedded = embedded.squeeze(1)
	    output = output.squeeze(1)
	    weighted = weighted.squeeze(1)
	    prediction = self.fc_out(torch.cat((output, weighted, embedded), dim=1))
	    return prediction, hidden.squeeze(0)
class Seq2Seq(nn.Module):
	def init(self, encoder, decoder, device):
		super().init()
		self.encoder = encoder
	    self.decoder = decoder
	    self.device = device

	def forward(self, src, trg, teacher_forcing_ratio=0.5):
	    batch_size = src.shape[0]
	    max_len = trg.shape[1]
	    trg_vocab_size = self.decoder.output_dim
	    outputs = torch.zeros(batch_size, max_len, trg_vocab_size).to(self.device)
	    encoder_outputs, hidden = self.encoder(src)
	    input = trg[:, 0]
	    for t in range(1, max_len):
	        output, hidden = self.decoder(input, hidden, encoder_outputs)
	        outputs[:, t, :] = output
	        teacher_force = random.random() < teacher_forcing_ratio
	        top1 = output.argmax(1)
	        input = trg[:, t] if teacher_force else top1
	    return outputs
# 定义训练函数
def train(model, iterator, optimizer, criterion, clip, device):
	model.train()
	epoch_loss = 0
	for i, batch in enumerate(iterator):
		src = batch.src.to(device)
		trg = batch.trg.to(device)
		optimizer.zero_grad()
		output = model(src, trg)
		output_dim = output.shape[-1]
		output = output[:, 1:].reshape(-1, output_dim)
		trg = trg[:, 1:].reshape(-1)
		loss = criterion(output, trg)
		loss.backward()
		torch.nn.utils.clip_grad_norm_(model.parameters(), clip)
		optimizer.step()
		epoch_loss += loss.item()
	return epoch_loss / len(iterator)

# 定义测试函数
def evaluate(model, iterator, criterion, device):
	model.eval()
	epoch_loss = 0
	with torch.no_grad():
		for i, batch in enumerate(iterator):
			src = batch.src.to(device)
			trg = batch.trg.to(device)
			output = model(src, trg, 0)
			output_dim = output.shape[-1]
			output = output[:, 1:].reshape(-1, output_dim)
			trg = trg[:, 1:].reshape(-1)
			loss = criterion(output, trg)
			epoch_loss += loss.item()
	return epoch_loss / len(iterator)
# 设置训练参数
INPUT_DIM = len(SRC.vocab)
OUTPUT_DIM = len(TRG.vocab)
ENC_EMB_DIM = 256
DEC_EMB_DIM = 256
ENC_HID_DIM = 512
DEC_HID_DIM = 512
ENC_DROPOUT = 0.5
DEC_DROPOUT = 0.5
LEARNING_RATE = 0.001
CLIP = 1
BATCH_SIZE = 128
NUM_EPOCHS = 10
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
train_iterator, valid_iterator, test_iterator = BucketIterator.splits((train_data, valid_data, test_data), batch_size=BATCH_SIZE, device=device, sort_within_batch=True, sort_key=lambda x: len(x.src))

# 对抗训练循环
epsilon = 0.1
for epoch in range(NUM_EPOCHS):
	train_loss = train(model, train_iterator, optimizer, criterion, CLIP, device)
	valid_loss = evaluate(model, valid_iterator, criterion, device)
	print('Epoch: {} Train Loss: {} Valid Loss: {}'.format(epoch+1, train_loss, valid_loss))
	for i, batch in enumerate(train_iterator):
		src = batch.src.to(device)
		trg = batch.trg.to(device)
		output = model(src, trg)
		output_dim = output.shape[-1]
		output = output[:, 1:].reshape(-1, output_dim)
		trg = trg[:, 1:].reshape(-1)
		loss = criterion(output, trg)
		loss.backward()
		input_adv = src.detach() + epsilon * torch.sign(src.grad)
		input_adv = torch.clamp(input_adv, 0, 1)
		src_adv = torch.autograd.Variable(input_adv, requires_grad=True)
		trg_adv = trg.detach()
		output_adv = model(src_adv, trg_adv)
		output_dim_adv = output_adv.shape[-1]
		output_adv = output_adv[:, 1:].reshape(-1, output_dim_adv)
		trg_adv = trg_adv[:, 1:].reshape(-1)
		loss_adv = criterion(output_adv, trg_adv)
		loss_adv.backward()
		optimizer.step()
		optimizer.zero_grad()

上述代码中，我们首先加载了Multi30k数据集，然后构建了源语言和目标语言的词汇表。接着定义了Seq2Seq模型，包括编码器和解码器。在训练和测试函数中，我们使用交叉熵损失函数进行训练和测试。在对抗训练循环中，我们使用FGSM生成对抗样本，并对模型进行训练。

结论

本文介绍了自然语言处理领域的对抗训练方法，包括文本分类、命名实体识别和机器翻译任务。对抗训练可以提高模型的鲁棒性，从而使其能够更好地处理噪声和干扰。我们使用了PyTorch深度学习框架实现了对抗训练，并提供了相应的代码示例。

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