意图识别及槽填充联合模型的改进 cnn-self-attention

一.简单介绍

此模型是对Convolutional Sequence to Sequence Learning中的encoder部分进行的改进。

原模型是用于机器翻译，这里我将稍加修改用来做问答中的slot filling和intent detection联合建模。

整体修改主要有以下几点：

1.使用多个size的卷积核进行多特征提取。

2.加入了多头attention进行特征提取。

3.增加了池化。

说明：

1.句子token和其对应的position进行embedding。

2.embedding进入几个不同size卷积核，这时是3个。将这些卷积后的特征进行拼接。

3.embedding进入一个多头attention层。

4.以上2，3的输出进行拼接。

5.经过一层池化后进行意图识别。

6.直接槽识别。

原模型的encoder的部分可参考：https://www.cnblogs.com/little-horse/p/14462023.html

multi-head attention部分：

二.代码

完整项目见：(https://github.com/jiangnanboy/intent_detection_and_slot_filling/blob/master/model5)

'''
这里将卷积后的特征与经过多头注意力后的特征进行融合
'''
class CNNAttention(nn.Module):
    def __init__(self, input_dim, intent_out, slot_out, hid_dim, n_layers, kernel_size, dropout, src_pad_idx, n_heads, max_length=50):
        super(CNNAttention, self).__init__()
        for kernel in kernel_size:
            assert kernel % 2 == 1,'kernel size must be odd!' # 卷积核size为奇数，方便序列两边pad处理
        
        self.src_pad_idx = src_pad_idx
        
        self.scale = torch.sqrt(torch.FloatTensor([0.5])).to(device) # 确保整个网络的方差不会发生显著变化
        
        self.tok_embedding = nn.Embedding(input_dim, hid_dim) # token编码
        self.pos_embedding = nn.Embedding(max_length, hid_dim) # token的位置编码
        
        self.hid2hid = nn.Linear(hid_dim * 2, hid_dim) # 线性层，从2 * hid_dim转为hid_dim
        
        # 不同的kernel_size
  
        self.conv_1 = nn.ModuleList([nn.Conv1d(in_channels=hid_dim,
                                                  out_channels=2*hid_dim, # 卷积后输出的维度，这里2*hid_dim是为了后面的glu激活函数
                                                  kernel_size=kernel_size[0],
                                                  padding=(kernel_size[0] - 1)//2) # 序列两边补0个数，保持维度不变
                                                  for _ in range(n_layers)])
        self.conv_2 = nn.ModuleList([nn.Conv1d(in_channels=hid_dim,
                                                  out_channels=2*hid_dim, # 卷积后输出的维度，这里2*hid_dim是为了后面的glu激活函数
                                                  kernel_size=kernel_size[1],
                                                  padding=(kernel_size[1] - 1)//2) # 序列两边补0个数，保持维度不变
                                                  for _ in range(n_layers)])
        self.conv_3 = nn.ModuleList([nn.Conv1d(in_channels=hid_dim,
                                                  out_channels=2*hid_dim, # 卷积后输出的维度，这里2*hid_dim是为了后面的glu激活函数
                                                  kernel_size=kernel_size[2],
                                                  padding=(kernel_size[2] - 1)//2) # 序列两边补0个数，保持维度不变
                                                  for _ in range(n_layers)])
        
        # 几个卷积模块转换维度
        self.convhid2hid = nn.Linear(len(kernel_size) * hid_dim, hid_dim)
        
        # 多头注意力模块
        self.self_attention = MultiHeadAttentionLayer(hid_dim, n_heads, dropout)
        
        self.dropout = nn.Dropout(dropout)
        
        # intent detection 意图识别
        self.intent_output = nn.Linear(hid_dim, intent_out)
        
         # slot filling，槽填充
        self.slot_out = nn.Linear(hid_dim, slot_out)
    
    def make_src_mask(self, src):
        # src: [batch_size, src_len]
        src_mask = (src != self.src_pad_idx).unsqueeze(1).unsqueeze(2) # [batch_size, 1, 1, src_len]
        
        return src_mask
        
    def forward(self, src):
        # src: [batch_size, src_len]
        # src_mask: [batch_size, src_len]
        batch_size = src.shape[0]
        src_len = src.shape[1]
        
        src_mask = self.make_src_mask(src) # [batch_size, 1, 1, src_len]
        
        # 创建token位置信息
        pos = torch.arange(src_len).unsqueeze(0).repeat(batch_size, 1).to(device) # [batch_size, src_len]
        
        # 对token与其位置进行编码
        tok_embedded = self.tok_embedding(src) # [batch_size, src_len, hid_dim]
        pos_embedded = self.pos_embedding(pos.long()) # [batch_size, src_len, hid_dim]
        
        # 对token embedded和pos_embedded逐元素加和
        embedded = self.dropout(tok_embedded + pos_embedded) # [batch_size, src_len, hid_dim]
        
        # 转变维度，卷积在输入数据的最后一维进行
        conv_input = embedded.permute(0, 2, 1) # [batch_size, hid_dim, src_len]
        
        # 第一个kernel_size
        conved_input = conv_input
        for i, conv in enumerate(self.conv_1):
            # 进行卷积
            conved1 = conv(self.dropout(conved_input)) # [batch_size, 2*hid_dim, src_len]

            # 进行激活glu
            conved1 = F.glu(conved1, dim=1) # [batch_size, hid_dim, src_len]

            # 进行残差连接
            conved1 = (conved1 + conved_input) * self.scale # [batch_size, hid_dim, src_len]

            # 作为下一个卷积块的输入
            conved_input = conved1
        
        combine_conv_module = conved1
        
        # 第二个kernel_size
        conved_input = conv_input
        for i, conv in enumerate(self.conv_2):
            # 进行卷积
            conved2 = conv(self.dropout(conved_input)) # [batch_size, 2*hid_dim, src_len]

            # 进行激活glu
            conved2 = F.glu(conved2, dim=1) # [batch_size, hid_dim, src_len]

            # 进行残差连接
            conved2 = (conved2 + conved_input) * self.scale # [batch_size, hid_dim, src_len]

            # 作为下一个卷积块的输入
            conved_input = conved2
            
        combine_conv_module = torch.cat([combine_conv_module, conved2], dim = 1)
        
        # 第三个kernel_size
        conved_input = conv_input
        for i, conv in enumerate(self.conv_3):
            # 进行卷积
            conved3 = conv(self.dropout(conved_input)) # [batch_size, 2*hid_dim, src_len]

            # 进行激活glu
            conved3 = F.glu(conved3, dim=1) # [batch_size, hid_dim, src_len]

            # 进行残差连接
            conved3 = (conved3 + conved_input) * self.scale # [batch_size, hid_dim, src_len]

            # 作为下一个卷积块的输入
            conved_input = conved3
            
        combine_conv_module = torch.cat([combine_conv_module, conved3], dim = 1)
        
        
        
        conved = self.convhid2hid(combine_conv_module.permute(0, 2, 1)) # [batch_size, src_len, hid_dim]
        
        # 这里在所有卷积之后增加了一个多头自注意力层，它的输入是
        self_attention, _ = self.self_attention(embedded, embedded, embedded, src_mask) # [batch_size, query_len, hid_dim]
        
        # 拼接卷积后的特征与多头注意力后的特征
        combined_conv_attention = torch.cat([conved, self_attention], dim=2) # [batch_size, query_len, 2*hid_dim]
        
        # 经过一线性层，将2*hid_dim转为hid_dim，作为输出的特征
        conved = self.hid2hid(combined_conv_attention) # [batch_size, query_len, hid_dim]
        
        # 又是一个残差连接，逐元素加和输出，作为encoder的联合输出特征
        combined = (conved + embedded) * self.scale # [batch_size, src_len, hid_dim]
        
        # 意图识别,加一个平均池化,池化后的维度是：[batch_size, hid_dim]
        intent_output = self.intent_output(self.dropout(F.max_pool1d(combined.permute(0, 2, 1), combined.shape[1]).squeeze())) # [batch_size, intent_dim]
    
        # 槽填充
        slot_output = self.slot_out(self.dropout(combined)) # [batch_size, trg_len, output_dim]
        
        return intent_output, slot_output
 
'''
多头注意力multi-head attention
'''
class MultiHeadAttentionLayer(nn.Module):
    def __init__(self, hid_dim, n_heads, dropout):
        super(MultiHeadAttentionLayer, self).__init__()
        
        assert hid_dim % n_heads == 0
        
        self.hid_dim = hid_dim
        self.n_heads = n_heads
        self.head_dim = hid_dim // n_heads
        
        self.fc_q = nn.Linear(hid_dim, hid_dim)
        self.fc_k = nn.Linear(hid_dim, hid_dim)
        self.fc_v = nn.Linear(hid_dim, hid_dim)
        
        self.fc_o = nn.Linear(hid_dim, hid_dim)
        
        self.dropout = nn.Dropout(dropout)
        
        self.scale = torch.sqrt(torch.FloatTensor([self.hid_dim])).to(device) # 缩放因子
        
    def forward(self, query, key, value, mask=None):
        '''
        query: [batch_size, query_len, hid_dim]
        key: [batch_size, key_len, hid_dim]
        value: [batch_size, value_len, hid_dim]
        '''
        batch_size = query.shape[0]
        
        Q = self.fc_q(query) # [batch_size, query_len, hid_dim]
        K = self.fc_k(key) # [batch_size, key_len, hid_dim]
        V = self.fc_v(value) # [batch_size, value_len, hid_dim]
        
        Q = Q.view(batch_size, -1, self.n_heads, self.head_dim).permute(0, 2, 1, 3) # [batch_size, n_heads, query_len, head_dim]
        K = K.view(batch_size, -1, self.n_heads, self.head_dim).permute(0, 2, 1, 3) # [batch_size, n_heads, key_len, head_dim]
        V = V.view(batch_size, -1, self.n_heads, self.head_dim).permute(0, 2, 1, 3) # [batch_size, n_heads, value_len, head_dim]
        
        # [batch_size, n_heads, query_len, head_dim] * [batch_size, n_heads, head_dim, key_len]
        energy = torch.matmul(Q, K.permute(0, 1, 3, 2)) / self.scale # [batch_size, n_heads, query_len, key_len]
        
        if mask != None:
            energy = energy.masked_fill(mask == 0, -1e10)
        
        attention = torch.softmax(energy, dim=-1) # [batch_size, n_heads, query_len, key_len]
        
        # [batch_size, n_heads, query_len, key_len] * [batch_size, n_heads, value_len, head_dim]
        x = torch.matmul(self.dropout(attention), V) # [batch_size, n_heads, query_len, head_dim]
        
        x = x.permute(0, 2, 1, 3).contiguous() # [batch_size, query_len, n_heads, head_dim]
        
        x = x.view(batch_size, -1, self.hid_dim) # [batch_size, query_len, hid_dim]
        
        x = self.fc_o(x) # [batch_size, query_len, hid_dim]
        
        return x, attention

loss:

predict: