文本生成任务之营销文本生成(Seq2seq+attention、Pointer Generator Network、Converage、Beam Search、优化技巧、文本增强)

文章目录

  • 引言
  • 项目任务简介
  • 0. 数据预处理
    • 0.1 将json文件转化成txt文件
    • 0.2 词典处理
    • 0.3 自定义数据集SampleDataset(Dataset类)
    • 0.4 生成Dataloader进行训练
  • 1. seq2seq+Attention
    • 1.1 Encoder
    • 1.2 Decoder
    • 1.3 Attention
    • 1.4 ReduceState
    • 1.5 Seq2seq的整体Forward
  • 2. PGN+coverage
    • 2.1 Encoder
    • 2.2 Decoder
    • 2.3 Attention
    • 2.4 ReduceState
    • 2.5 get_final_distribution函数
    • 2.6 PGN的整体Forward
  • 3. 模型训练+可视化
    • 3.1 pytorch训练模板
    • 3.2 TensorBoard可视化
  • 4. 模型解码
    • 4.1 实现Greedy search
    • 4.2 实现Beam search
  • 5. Rouge评估
  • 6. 数据增强
    • 6.1 单词替换
    • 6.2 回译
    • 6.3 自助式样本生成
  • 7. 优化技巧
    • 7.1 Weight tying
    • 7.2 Scheduled sampling
  • 8. 实验结果

引言

文本生成(Text Generation)具体可以细分成文本摘要、机器翻译、故事续写等几种任务。本项目主要用到的是文本摘要(Summarization)的技术。我们生成摘要的方式是选取其中关键的句子摘抄下来。相反,生成式摘要则是希望通过学习原文的语义信息后相应地生成一段较短但是能反应其核心思想的文本作为摘要。生成式
摘要相较于抽取式摘要更加灵活,但也更加难以实现。本项目我们将会先用生成式摘要的方法构建一个 Seq2seq+Attention的模型作为 baseline,然后构建一个结合了生成式和抽取式两种方法的 Pointer-Generator Network(PGN)模型。

在本项目中,可以学习到

  • 熟练掌握(Seq2seq、Attention、LSTM、PGN 、converage等模型)的实现。
  • 熟练掌握如何训练神经网络(调参、debug、可视化)。
  • 熟练掌握如何实现 Beam Search 算法来生成文本。
  • 熟练掌握文本生成任务的评估方法
  • 掌握深度学习训练的一些优化技巧(Scheduled sampling、Weight tying等)。
  • 了解如何使用多种文本增强技术处理少样本问题。

项目任务简介

文本生成任务中,我们作为输入的原文称之为 source,待生成的目标文本称之为 target 或者 hypothesis,用来作为 target 好坏的参考文本称之为reference。在本项目的数据源来自于京东电商的发现好货栏目,source 主
要由三部分构成:1 是商品的标题,2 是商品的参数,3 是商品宣传图片里提取出来的宣传文案(借助OCR)。

参考文本如下图所示:

文本生成任务之营销文本生成(Seq2seq+attention、Pointer Generator Network、Converage、Beam Search、优化技巧、文本增强)_第1张图片

商品的标题:

商品的参数:

文本生成任务之营销文本生成(Seq2seq+attention、Pointer Generator Network、Converage、Beam Search、优化技巧、文本增强)_第2张图片

商品宣传图片:

文本生成任务之营销文本生成(Seq2seq+attention、Pointer Generator Network、Converage、Beam Search、优化技巧、文本增强)_第3张图片

0. 数据预处理

首先数据格式如下图所示,为一个json文件,里面的title、kb以及ocr为我们利用的source,reference可以看作我们的标签。

文本生成任务之营销文本生成(Seq2seq+attention、Pointer Generator Network、Converage、Beam Search、优化技巧、文本增强)_第4张图片

0.1 将json文件转化成txt文件

就是一个读取json,写入txt的文件,不再赘述。

json_path = os.path.join(abs_path, '服饰_50k.json')
with open(json_path, 'r', encoding='utf8') as file:
    jsf = json.load(file)
for jsobj in jsf.values():
    title = jsobj['title'] + ' '  # Get title.
    kb = dict(jsobj['kb']).items()  # Get attributes.
    kb_merged = ''
    for key, val in kb:
        kb_merged += key+' '+val+' '  # Merge attributes.
    ocr = ' '.join(list(jieba.cut(jsobj['ocr'])))  # Get OCR text.
    texts = []
    texts.append(title + ocr + kb_merged)  # Merge them.
    reference = ' '.join(list(jieba.cut(jsobj['reference'])))
    for text in texts:
        sample = text+''+reference  # Seperate source and reference.
        samples.add(sample)
write_path = os.path.join(abs_path, '../files/samples.txt')
write_samples(samples, write_path)

0.2 词典处理

add_words函数:向词典⾥加⼊⼀个新词,需要完成对word2index、index2word和word2count三个变量的更新。

class Vocab(object):
    def __init__(self):
        self.word2index = {}
        self.word2count = Counter()
        self.reserved = ['', '', '', '']
        self.index2word = self.reserved[:]
        self.embeddings = None
    def add_words(self, words):
        """Add a new token to the vocab and do mapping between word and index.
        """
        for word in words:
            if word not in self.word2index:
                self.word2index[word] = len(self.index2word)
                self.index2word.append(word)
        self.word2count.update(words)

build_vocab函数:需要实现控制数据集词典的⼤⼩(从config.max_vocab_size)读取这⼀参数。我们这里使⽤python的collection模块中的Counter来做。

def build_vocab(self, embed_file: str = None) -> Vocab:
        """Build the vocabulary for the data set.
        """
        # word frequency
        word_counts = Counter()
        count_words(word_counts, [src + tgr for src, tgr in self.pairs])
        vocab = Vocab()
        # Filter the vocabulary by keeping only the top k tokens in terms of
        # word frequncy in the data set
        for word, count in word_counts.most_common(config.max_vocab_size):
            vocab.add_words([word])
        if embed_file is not None:
            count = vocab.load_embeddings(embed_file)
            print("%d pre-trained embeddings loaded." % count)
        return vocab


def count_words(counter, text):
    '''Count the number of occurrences of each word in a set of text'''
    for sentence in text:
        for word in sentence:
            counter[word] += 1

0.3 自定义数据集SampleDataset(Dataset类)

我们知道用dataset和dataloader来进行数据加载读取训练和方便,这里我们自定义了SampleDataset,如下所示,其中我们要具体讲一讲__getitem__函数。

class SampleDataset(Dataset):
    """The class represents a sample set for training.
    """
    def __init__(self, data_pair, vocab):
        self.src_sents = [x[0] for x in data_pair]
        self.trg_sents = [x[1] for x in data_pair]
        self.vocab = vocab
        self._len = len(data_pair)
    def __getitem__(self, index):
        x, oov = source2ids(self.src_sents[index], self.vocab)
        return {
            'x': [self.vocab.SOS] + x + [self.vocab.EOS],
            'OOV': oov,
            'len_OOV': len(oov),
            'y': [self.vocab.SOS] +
            abstract2ids(self.trg_sents[index],
                         self.vocab, oov) + [self.vocab.EOS],
            'x_len': len(self.src_sents[index]),
            'y_len': len(self.trg_sents[index])
        }
    def __len__(self):
        return self._len

其中我们的__getitem__函数就是想取第index个数据,我们这里想要做的是返回第index个文本数据的id token,和以前不同的是这个id token包括了对OOV的处理,我们具体来说。

对于source文本,我们通过source2ids将字符串映射成id。将源单词映射到它们的id并返回源代码中的oov列表,OOV由它们的临时源OOV编号表示。如果词表大小是5000并且源单词中含有三个OOV,那么OOV编号的大小是5001、5002和5003。

def source2ids(source_words, vocab):
    ids = []
    oovs = []
    unk_id = vocab.UNK
    for w in source_words:
        i = vocab[w]
        if i == unk_id:  
            if w not in oovs:  
                oovs.append(w)
            # This is 0 for the first source OOV, 1 for the second source OOV
            oov_num = oovs.index(w)
            # This is e.g. 20000 for the first source OOV, 50001 for the second
            ids.append(vocab.size() + oov_num)
        else:
            ids.append(i)
    return ids, oovs

对于reference文本,我们通过成abstract2ids函数将reference文本映射成Id。由于PGN可以⽣成在source⾥⾯出现过的OOV tokens,所以这次我们对reference的token ids需要换⼀种映射⽅式,即将在source⾥出现过的OOV tokens也记录下来并给⼀个临时的id,⽽不是直接替换为“”,以便在训练时计算损失更加准确。

def abstract2ids(abstract_words, vocab, source_oovs):
    """Map tokens in the abstract (reference) to ids.
       OOV tokens in the source will be remained.
    """
    ids = []
    unk_id = vocab.UNK
    for w in abstract_words:
        i = vocab[w]
        if i == unk_id:  # If w is an OOV word
            if w in source_oovs:  # If w is an in-source OOV
                # Map to its temporary source OOV number
                vocab_idx = vocab.size() + source_oovs.index(w)
                ids.append(vocab_idx)
            else:  # If w is an out-of-source OOV
                ids.append(unk_id)  # Map to the UNK token id
        else:
            ids.append(i)
    return ids

0.4 生成Dataloader进行训练

Dataloader可以更方便的在数据集中取batch进行批训练,其中最重要的是collate_fn函数,它的作用是将数据集拆分为多个批,并对每个批进行填充;其中我们确定一个batch的最大长度,是根据sort_batch_by_len函数实现的。

train_data = SampleDataset(dataset.pairs, v)
val_data = SampleDataset(val_dataset.pairs, v)
train_dataloader = DataLoader(dataset=train_data,
                                  batch_size=config.batch_size,
                                  shuffle=True,
                                  collate_fn=collate_fn)

def collate_fn(batch):
    """Split data set into batches and do padding for each batch.
    """
    def padding(indice, max_length, pad_idx=0):
        pad_indice = [item + [pad_idx] * max(0, max_length - len(item)) for item in indice]
        return torch.tensor(pad_indice)
    data_batch = sort_batch_by_len(batch)
    x = data_batch["x"]
    x_max_length = max([len(t) for t in x])
    y = data_batch["y"]
    y_max_length = max([len(t) for t in y])
    OOV = data_batch["OOV"]
    len_OOV = torch.tensor(data_batch["len_OOV"])
    x_padded = padding(x, x_max_length)
    y_padded = padding(y, y_max_length)
    x_len = torch.tensor(data_batch["x_len"])
    y_len = torch.tensor(data_batch["y_len"])
    return x_padded, y_padded, x_len, y_len, OOV, len_OOV

1. seq2seq+Attention

我们的基线模型如下图所示。文章中的token被逐个送入编码器(单层双向LSTM),产生一系列编码器隐藏状态 h i h_i hi。在每个步骤t中,解码器(单层单向LSTM)接收前一个单词的单词嵌入(在训练时,这是参考摘要的前一个单词;在测试时,它是解码器发出的前一个单词),并且具有解码器状态 s t s_t st

文本生成任务之营销文本生成(Seq2seq+attention、Pointer Generator Network、Converage、Beam Search、优化技巧、文本增强)_第5张图片

注意分布 a t a_t at的计算如Bahdanau等人所述:
在这里插入图片描述
加权求和生成内容向量:
在这里插入图片描述

解码端得到单词分布:
在这里插入图片描述

1.1 Encoder

class Encoder(nn.Module):
    def __init__(self,vocab_size, embed_size, hidden_size, rnn_drop: float = 0):
        super(Encoder, self).__init__()
        self.embedding = nn.Embedding(vocab_size, embed_size)
        self.hidden_size = hidden_size
        self.lstm = nn.LSTM(embed_size,
                            hidden_size,
                            bidirectional=True,
                            dropout=rnn_drop,
                            batch_first=True)
    def forward(self, x):
        """Define forward propagation for the endoer.
        """
        embedded = self.embedding(x)
        output, hidden = self.lstm(embedded)
        return output, hidden

1.2 Decoder

class Decoder(nn.Module):
    def __init__(self, vocab_size, embed_size, hidden_size, enc_hidden_size=None, is_cuda=True):
        super(Decoder, self).__init__()
        self.embedding = nn.Embedding(vocab_size, embed_size)
        self.DEVICE = torch.device('cuda') if is_cuda else torch.device('cpu')
        self.vocab_size = vocab_size
        self.hidden_size = hidden_size
        self.lstm = nn.LSTM(embed_size, hidden_size, batch_first=True)
        self.W1 = nn.Linear(self.hidden_size * 3, self.hidden_size)
        self.W2 = nn.Linear(self.hidden_size, vocab_size)

    def forward(self, x_t, decoder_states, context_vector):
        """Define forward propagation for the decoder.
        """
        decoder_emb = self.embedding(x_t)
        decoder_output, decoder_states = self.lstm(decoder_emb, decoder_states)
        # concatenate context vector and decoder state
        decoder_output = decoder_output.view(-1, config.hidden_size)
        concat_vector = torch.cat([decoder_output, context_vector], dim=-1)
        # calculate vocabulary distribution
        FF1_out = self.W1(concat_vector)
        FF2_out = self.W2(FF1_out)
        p_vocab = F.softmax(FF2_out, dim=1)
        h_dec, c_dec = decoder_states
        s_t = torch.cat([h_dec, c_dec], dim=2)
        return p_vocab, decoder_states

1.3 Attention

a. 处理decoder的隐状态 h h h c c c,将⼆者拼接得到 s t s_t st,并处理成合理的shape。
b. 参考论⽂中的公式(1)和(2),实现attention weights的计算。
c. 由于训练过程中会对batch中的样本进⾏padding,对于进⾏了padding的输⼊我们需要把填充的位置的attention weights给过滤掉(padding mask),然后对剩下位置的attention weights进⾏归⼀化。
d. 根据论⽂中的公式(3)计算context vector

class Attention(nn.Module):
    def __init__(self, hidden_units):
        super(Attention, self).__init__()
        # Define feed-forward layers.
        self.Wh = nn.Linear(2*hidden_units, 2*hidden_units, bias=False)
        self.Ws = nn.Linear(2*hidden_units, 2*hidden_units)

    def forward(self,
                decoder_states,
                encoder_output,
                x_padding_masks,
                coverage_vector):
        """Define forward propagation for the attention network.
        """
        # Concatenate h and c to get s_t and expand the dim of s_t.
        h_dec, c_dec = decoder_states
        s_t = torch.cat([h_dec, c_dec], dim=2)
        s_t = s_t.transpose(0, 1)
        s_t = s_t.expand_as(encoder_output).contiguous()
        # calculate attention scores
        encoder_features = self.Wh(encoder_output.contiguous())
        decoder_features = self.Ws(s_t)
        att_inputs = encoder_features + decoder_features
        score = self.v(torch.tanh(att_inputs))
        attention_weights = F.softmax(score, dim=1).squeeze(2)
        attention_weights = attention_weights * x_padding_masks
        # Normalize attention weights after excluding padded positions.
        normalization_factor = attention_weights.sum(1, keepdim=True)
        attention_weights = attention_weights / normalization_factor
        context_vector = torch.bmm(attention_weights.unsqueeze(1),
                                   encoder_output)
        context_vector = context_vector.squeeze(1)
        return context_vector, attention_weights

1.4 ReduceState

我们的encoder⽤了BiLSTM,⽽decoder⽤的是单向的LSTM,使⽤encoder的输出作为decoder初始隐状态时,需要对encoder的隐状态进⾏降维。实现的⽅式可以有多种,可以对两个⽅向的隐状态简单相加,也可以定义⼀个前馈层来做这个事情。这⾥我们⽤⼀个ReduceState的模块以简单相加的形式来实现,具体⻅代码。

class RetduceSate(nn.Module):
    def __init__(self):
        super(ReduceState, self).__init__()

    def forward(self, hidden):
        """The forward propagation of reduce state module.
        """
        h, c = hidden
        h_reduced = torch.sum(h, dim=0, keepdim=True)
        c_reduced = torch.sum(c, dim=0, keepdim=True)
        return (h_reduced, c_reduced)

1.5 Seq2seq的整体Forward

  1. 对输⼊序列x进⾏处理,对于oov的token,需要将他们的index转换成 UNK token 。
  2. ⽣成输⼊序列x的padding mask 。
  3. 得到encoder的输出和隐状态,并对隐状态进⾏降维后作为decoder的初始隐状态。
  4. 对于每⼀个time step,以输⼊序列y的 y t y_t yt作为输⼊, y t + 1 y_{t+1} yt+1作为target,计算attention,然后⽤
    decoder得到 p v o c a b p_{vocab} pvocab,找到target对应的词在 p v o c a b p_{vocab} pvocab中对应的概率 t a r g e t p r o b s target_{probs} targetprobs ,然后计算time step t t t的损失,然后加上padding mask。
  5. 计算整个序列的平均loss。
  6. 计算整个batch的平均loss并返回。
class Seq2seq(nn.Module):
    def __init__(self, v):
        super(Seq2seq, self).__init__()
        self.v = v
        self.DEVICE = config.DEVICE
        self.attention = Attention(config.hidden_size)
        self.encoder = Encoder(len(v),config.embed_size,config.hidden_size,)
        self.decoder = Decoder(len(v),config.embed_size,config.hidden_size,)
        self.reduce_state = ReduceState()

 
    def forward(self, x, x_len, y, len_oovs, batch, num_batches):
        """Define the forward propagation for the model.
        """
        x_copy = replace_oovs(x, self.v)
        x_padding_masks = torch.ne(x, 0).byte().float()
        encoder_output, encoder_states = self.encoder(x_copy)
        # Reduce encoder hidden states.
        decoder_states = self.reduce_state(encoder_states)
        # Calculate loss for every step.
        step_losses = []
        for t in range(y.shape[1]-1):
            # Do teacher forcing.
            x_t = y[:, t]
            x_t = replace_oovs(x_t, self.v)
            y_t = y[:, t+1]
            # Get context vector from the attention network.
            context_vector, attention_weights = self.attention(decoder_states, encoder_output, x_padding_masks, coverage_vector)
            # Get vocab distribution and hidden states from the decoder.
            p_vocab, decoder_states= self.decoder(x_t.unsqueeze(1), decoder_states, context_vector)
            # Get the probabilities predict by the model for target tokens.
            y_t = replace_oovs(y_t, self.v)
            target_probs = torch.gather(p_vocab, 1, y_t.unsqueeze(1))
            target_probs = target_probs.squeeze(1)
            # Apply a mask such that pad zeros do not affect the loss
            mask = torch.ne(y_t, 0).byte()
            # Do smoothing to prevent getting NaN loss because of log(0).
            loss = -torch.log(target_probs + config.eps)
            mask = mask.float()
            loss = loss * mask
            step_losses.append(loss)

        sample_losses = torch.sum(torch.stack(step_losses, 1), 1)
        # get the non-padded length of each sequence in the batch
        seq_len_mask = torch.ne(y, 0).byte().float()
        batch_seq_len = torch.sum(seq_len_mask, dim=1)
        # get batch loss by dividing the loss of each batch
        # by the target sequence length and mean
        batch_loss = torch.mean(sample_losses / batch_seq_len)
        return batch_loss

2. PGN+coverage

我们这里说一下seq2seq+attention的缺点,上面这方法虽然可以自由的生成文本,但是表现出很多表现不佳的行为,包括但不限于不准确地再现事实细节、无法处理词汇表外(OOV)单词以及生成重复的单词。

指针生成器网络(PGN)有助于通过指针从源文本复制单词,这提高了OOV单词的准确性和处理能力,同时保留了生成新词的能力。这个网络可以看作是提取和抽象方法之间的平衡。

我们还加入了Coverage vector,从神经机器翻译,我们用来跟踪和控制源文件的重复范围。我们证明Converage对于消除重复是非常有效的。

具体框架如下图所示:
文本生成任务之营销文本生成(Seq2seq+attention、Pointer Generator Network、Converage、Beam Search、优化技巧、文本增强)_第6张图片
对于每个译码器时间步,计算生成概率 p g e n ∈ [ 0 , 1 ] p_{gen}∈[0,1] pgen[0,1],该概率加权从词汇表生成单词的概率,而不是从源文本复制单词的概率。对词汇分布和注意分布进行加权和求和,得到最终分布,并据此进行预测。
生成概率计算如下:
在这里插入图片描述

单词概率计算如下:
在这里插入图片描述

我们的converage模型中,我们保持覆盖向量 C t C_t Ct,它是所有先前解码器时间步的注意力分布的总和:
在这里插入图片描述

Converage向量被用作注意力机制的额外输入:
在这里插入图片描述
我们定义一个覆盖损失,以惩罚重复到同一位置分配过多的注意力:
在这里插入图片描述

2.1 Encoder

Encoder端没有变化,这里不再赘述。

2.2 Decoder

多了一个实现 p g e n p_{gen} pgen的计算,代码如下所示:

    def forward(self, x_t, decoder_states, context_vector):
        """Define forward propagation for the decoder.
        """
        decoder_emb = self.embedding(x_t)
        decoder_output, decoder_states = self.lstm(decoder_emb, decoder_states)
        # concatenate context vector and decoder state
        decoder_output = decoder_output.view(-1, config.hidden_size)
        concat_vector = torch.cat(
            [decoder_output,
             context_vector],
            dim=-1)
        # calculate vocabulary distribution
        FF1_out = self.W1(concat_vector)
        FF2_out = self.W2(FF1_out)
        p_vocab = F.softmax(FF2_out, dim=1)
        h_dec, c_dec = decoder_states
        s_t = torch.cat([h_dec, c_dec], dim=2)
        p_gen = None
        if config.pointer:
            # Calculate p_gen.
            x_gen = torch.cat([context_vector,s_t.squeeze(0),decoder_emb.squeeze(1)], dim=-1)
            p_gen = torch.sigmoid(self.w_gen(x_gen))
        return p_vocab, decoder_states, p_gen

2.3 Attention

多了两部分的改进:
a. 计算attention weights时加⼊coverage vector。
b. 对coverage vector进⾏更新。

    def forward(self, decoder_states, encoder_output, x_padding_masks, coverage_vector):
        """Define forward propagation for the attention network.
        """
        # Concatenate h and c to get s_t and expand the dim of s_t.
        h_dec, c_dec = decoder_states
        s_t = torch.cat([h_dec, c_dec], dim=2)
        s_t = s_t.transpose(0, 1)
        s_t = s_t.expand_as(encoder_output).contiguous()
        # calculate attention scores
        encoder_features = self.Wh(encoder_output.contiguous())
        decoder_features = self.Ws(s_t)
        att_inputs = encoder_features + decoder_features
        # Add coverage feature.
        if config.coverage:
            coverage_features = self.wc(coverage_vector.unsqueeze(2))  # wc c
            att_inputs = att_inputs + coverage_features
        score = self.v(torch.tanh(att_inputs))
        attention_weights = F.softmax(score, dim=1).squeeze(2)
        attention_weights = attention_weights * x_padding_masks

        # Normalize attention weights after excluding padded positions.
        normalization_factor = attention_weights.sum(1, keepdim=True)
        attention_weights = attention_weights / normalization_factor       
        context_vector = torch.bmm(attention_weights.unsqueeze(1), encoder_output)
        context_vector = context_vector.squeeze(1)
        # Update coverage vector.
        if config.coverage:
            coverage_vector = coverage_vector + attention_weights
        return context_vector, attention_weights, coverage_vector

2.4 ReduceState

ReduceState模块也没有变化。

2.5 get_final_distribution函数

所谓的 pointer 本质是根据 attention 的分布 (source 中每个 token 的概率分布)来挑选输出的词,是从
source 中 挑选最佳的 token 作为输出;所谓的 generator 的本质是根据 decoder 计算得到的字典概率
分布 P_vocab 来挑选输出的词,是从字典中挑 选最佳的 token 作为输出。

所以应该能发现:Attention 的分布和 P_vocab 的分布的⻓度和对应位置代表的 token 是不⼀样的,所以在计算 final distribution 的时候应该如何对应上呢? 这⾥推荐的⽅式是,先对 P_vocab 进⾏扩展,将 source 中的 oov 添 加到 P_vocab 的尾部,得到 P_vocab_extend 这样 attention weights 中的每⼀个 token 都能在 P_vocab_extend 中找到对应的位 置,然后 将对应的 attention weights 叠加到扩展后的 P_vocab_extend 中的对 应位置,得到 final distribution。

    def get_final_distribution(self, x, p_gen, p_vocab, attention_weights, max_oov):
        """Calculate the final distribution for the model.
        """
        batch_size = x.size()[0]
        # Clip the probabilities.
        p_gen = torch.clamp(p_gen, 0.001, 0.999)
        # Get the weighted probabilities.
        p_vocab_weighted = p_gen * p_vocab
        attention_weighted = (1 - p_gen) * attention_weights

        # Get the extended-vocab probability distribution    
        extension = torch.zeros((batch_size, max_oov)).float().to(self.DEVICE)
        p_vocab_extended = torch.cat([p_vocab_weighted, extension], dim=1)
        # Add the attention weights to the corresponding vocab positions.
        final_distribution =  p_vocab_extended.scatter_add_(dim=1, index=x, src=attention_weighted)
        return final_distribution

2.6 PGN的整体Forward

整体forward和Seq2seq+attention没有多少区别,多了一个计算Converage Loss 的过程。

def forward(self, x, x_len, y, len_oovs, batch, num_batches):
        """Define the forward propagation for the model.
        """
        x_copy = replace_oovs(x, self.v)
        x_padding_masks = torch.ne(x, 0).byte().float()
        encoder_output, encoder_states = self.encoder(x_copy)
        # Reduce encoder hidden states.
        decoder_states = self.reduce_state(encoder_states)
        # Initialize coverage vector.
        coverage_vector = torch.zeros(x.size()).to(self.DEVICE)
        # Calculate loss for every step.
        step_losses = []
        for t in range(y.shape[1]-1):
            # Do teacher forcing.
            x_t = y[:, t]
            x_t = replace_oovs(x_t, self.v)

            y_t = y[:, t+1]
            # Get context vector from the attention network.
            context_vector, attention_weights, next_coverage_vector = \
                self.attention(decoder_states,
                               encoder_output,
                               x_padding_masks,
                               coverage_vector)
            # Get vocab distribution and hidden states from the decoder.
            p_vocab, decoder_states, p_gen = self.decoder(x_t.unsqueeze(1),
                                                          decoder_states,
                                                          context_vector)

            final_dist = self.get_final_distribution(x,
                                                     p_gen,
                                                     p_vocab,
                                                     attention_weights,
                                                     torch.max(len_oovs))

            # Get the probabilities predict by the model for target tokens.
            target_probs = torch.gather(final_dist, 1, y_t.unsqueeze(1))
            target_probs = target_probs.squeeze(1)

            # Apply a mask such that pad zeros do not affect the loss
            mask = torch.ne(y_t, 0).byte()
            # Do smoothing to prevent getting NaN loss because of log(0).
            loss = -torch.log(target_probs + config.eps)

            # Add coverage loss.
            ct_min = torch.min(attention_weights, coverage_vector)
            cov_loss = torch.sum(ct_min, dim=1)
            loss = loss + config.LAMBDA * cov_loss
            coverage_vector = next_coverage_vector

            mask = mask.float()
            loss = loss * mask
            step_losses.append(loss)

        sample_losses = torch.sum(torch.stack(step_losses, 1), 1)
        # get the non-padded length of each sequence in the batch
        seq_len_mask = torch.ne(y, 0).byte().float()
        batch_seq_len = torch.sum(seq_len_mask, dim=1)

        # get batch loss by dividing the target sequence length and mean
        batch_loss = torch.mean(sample_losses / batch_seq_len)
        return batch_loss

在这里插入代码片

3. 模型训练+可视化

3.1 pytorch训练模板

optimizer = None # Choose an optimizer from torch.optim
batch_losses = []
for batch in batches:
	model.train() # Sets the module in training mode.
	optimizer.zero_grad() # Clear gradients.
	batch_loss = model(**params)# Calculate loss for a batch.
	batch_losses.append(loss.item())
	batch_loss.backward() # Backpropagation.
	optimizer.step() # Update weights.
epoch_loss = torch.mean(batch_losses)

3.2 TensorBoard可视化

  1. 创建⼀个SummaryWriter对象,调⽤add_scalar函数来记录损失,记得写完要调⽤close函数。
  2. 并且在适当的位置实现梯度剪裁(clip_grad_norm_函数)。

整体的代码如下:

    optimizer = optim.Adam(model.parameters(), lr=config.learning_rate)
    writer = SummaryWriter(config.log_path)
    # tqdm: A tool for drawing progress bars during training.
    with tqdm(total=config.epochs) as epoch_progress:
        for epoch in range(start_epoch, config.epochs):
            batch_losses = []  # Get loss of each batch.
            num_batches = len(train_dataloader)
            with tqdm(total=num_batches//100) as batch_progress:               
                for batch, data in enumerate(tqdm(train_dataloader)):
                    x, y, x_len, y_len, oov, len_oovs = data
                    model.train()  # Sets the module in training mode.
                    optimizer.zero_grad()  # Clear gradients.
                    # Calculate loss.
                    loss = model(x, x_len, y, len_oovs, batch=batch, num_batches=num_batches)
                    batch_losses.append(loss.item())
                    loss.backward()  # Backpropagation.
                    # Do gradient clipping to prevent gradient explosion.
                    clip_grad_norm_(model.encoder.parameters(),
                                    config.max_grad_norm)
                    clip_grad_norm_(model.decoder.parameters(),
                                    config.max_grad_norm)
                    clip_grad_norm_(model.attention.parameters(),
                                    config.max_grad_norm)
                    optimizer.step()  # Update weights.
                    # Output and record epoch loss every 100 batches.
                    if (batch % 100) == 0:
                        batch_progress.set_description(f'Epoch {epoch}')
                        batch_progress.set_postfix(Batch=batch,
                                                   Loss=loss.item())
                        batch_progress.update()
                        # Write loss for tensorboard.
                        writer.add_scalar(f'Average loss for epoch {epoch}',
                                          np.mean(batch_losses),
                                          global_step=batch)
            # Calculate average loss over all batches in an epoch.
            epoch_loss = np.mean(batch_losses)
            epoch_progress.set_description(f'Epoch {epoch}')
            epoch_progress.set_postfix(Loss=epoch_loss)
            epoch_progress.update()
            avg_val_loss = evaluate(model, val_data, epoch)
            print('training loss:{}'.format(epoch_loss), 'validation loss:{}'.format(avg_val_loss))

    writer.close()

文本生成任务之营销文本生成(Seq2seq+attention、Pointer Generator Network、Converage、Beam Search、优化技巧、文本增强)_第7张图片

4. 模型解码

4.1 实现Greedy search

这⼀块⽐较简单,⽤encoder编码输⼊,传递每⼀个time step的信息给decoder,计算attention,得到decoder的p_vocab,根据p_vocab选出概率最⼤的词作为下⼀个token。代码如下:

    def greedy_search(self,
                      x,
                      max_sum_len,
                      len_oovs,
                      x_padding_masks):
        """Function which returns a summary by always picking
        """

        # Get encoder output and states.
        encoder_output, encoder_states = self.model.encoder(
            replace_oovs(x, self.vocab))

        # Initialize decoder's hidden states with encoder's hidden states.
        decoder_states = self.model.reduce_state(encoder_states)

        # Initialize decoder's input at time step 0 with the SOS token.
        x_t = torch.ones(1) * self.vocab.SOS
        x_t = x_t.to(self.DEVICE, dtype=torch.int64)
        summary = [self.vocab.SOS]
        coverage_vector = torch.zeros((1, x.shape[1])).to(self.DEVICE)
        # Generate hypothesis with maximum decode step.
        while int(x_t.item()) != (self.vocab.EOS) \
                and len(summary) < max_sum_len:
            context_vector, attention_weights, coverage_vector = \
                self.model.attention(decoder_states,
                                     encoder_output,
                                     x_padding_masks,
                                     coverage_vector)
            p_vocab, decoder_states, p_gen = \
                self.model.decoder(x_t.unsqueeze(1),
                                   decoder_states,
                                   context_vector)
            final_dist = self.model.get_final_distribution(x,
                                                           p_gen,
                                                           p_vocab,
                                                           attention_weights,
                                                           torch.max(len_oovs))
            # Get next token with maximum probability.
            x_t = torch.argmax(final_dist, dim=1).to(self.DEVICE)
            decoder_word_idx = x_t.item()
            summary.append(decoder_word_idx)
            x_t = replace_oovs(x_t, self.vocab)
        return summary

4.2 实现Beam search

我们的实现对Beam search进行了优化(加入了Length normalization, Coverage normalization以及End of sentence normalization),关于Beam search,我的博客里也有详细介绍,大体可以分为三步:

首先,首先定义一个 Beam 类,作为一个存放候选序列的容器,属性需维护当前序列中的 token 以及对应的对数概率,同时还需维护跟当前 timestep 的 Decoder 相关的一些变量。此外,还需要给 Beam 类实现两个函数:一个 extend 函数用以扩展当前的序列(即添加新的 time step的 token 及相关变量);一个 score 函数用来计算当前序列的分数(在Beam类下的seq_score函数中有Length normalization以及Coverage normalization)。


class Beam(object):
    def __init__(self,
                 tokens,
                 log_probs,
                 decoder_states,
                 coverage_vector):
        self.tokens = tokens
        self.log_probs = log_probs
        self.decoder_states = decoder_states
        self.coverage_vector = coverage_vector

    def extend(self,
               token,
               log_prob,
               decoder_states,
               coverage_vector):
        return Beam(tokens=self.tokens + [token],
                    log_probs=self.log_probs + [log_prob],
                    decoder_states=decoder_states,
                    coverage_vector=coverage_vector)
    def seq_score(self):
        """
        This function calculate the score of the current sequence.
        """
        len_Y = len(self.tokens)
        # Lenth normalization
        ln = (5+len_Y)**config.alpha / (5+1)**config.alpha
        cn = config.beta * torch.sum(  # Coverage normalization
            torch.log(
                config.eps +
                torch.where(
                    self.coverage_vector < 1.0,
                    self.coverage_vector,
                    torch.ones((1, self.coverage_vector.shape[1])).to(torch.device(config.DEVICE))
                )
            )
        )
        score = sum(self.log_probs) / ln + cn
        return score

接着我们需要实现一个 best_k 函数,作用是将一个 Beam 容器中当前 time step 的变量传入 Decoder 中,计算出新一轮的词表概率分布,并从中选出概率最大的 k 个 token 来扩展当前序列(其中加入了End of sentence normalization),得到 k 个新的候选序列。

    def best_k(self, beam, k, encoder_output, x_padding_masks, x, len_oovs):
        """Get best k tokens to extend the current sequence at the current time step.
        """
        # use decoder to generate vocab distribution for the next token
        x_t = torch.tensor(beam.tokens[-1]).reshape(1, 1)
        x_t = x_t.to(self.DEVICE)

        # Get context vector from attention network.
        context_vector, attention_weights, coverage_vector = \
            self.model.attention(beam.decoder_states,
                                 encoder_output,
                                 x_padding_masks,
                                 beam.coverage_vector)
、
        p_vocab, decoder_states, p_gen = \
            self.model.decoder(replace_oovs(x_t, self.vocab),
                               beam.decoder_states,
                               context_vector)

        final_dist = self.model.get_final_distribution(x,
                                                       p_gen,
                                                       p_vocab,
                                                       attention_weights,
                                                       torch.max(len_oovs))
        # Calculate log probabilities.
        log_probs = torch.log(final_dist.squeeze())
        # EOS token penalty. Follow the definition in
        log_probs[self.vocab.EOS] *= \
            config.gamma * x.size()[1] / len(beam.tokens)
        log_probs[self.vocab.UNK] = -float('inf')
        # Get top k tokens and the corresponding logprob.
        topk_probs, topk_idx = torch.topk(log_probs, k)
        best_k = [beam.extend(x,
                  log_probs[x],
                  decoder_states,
                  coverage_vector) for x in topk_idx.tolist()]
        return best_k

最后我们实现主函数 beam_search。初始化encoder、attention和decoder的输⼊,然后对于每⼀个decodestep,对于现有的k个beam,我们分别利⽤best_k函数来得到各⾃最佳的k个extended beam,也就是每个decode step我们会得到k*k个新的beam,然后只保留分数最⾼的k个,作为下⼀轮需要扩展的k个beam。为了只保留分数最⾼的k个beam,我们可以⽤⼀个堆(heap)来实现,堆的中只保存k个节点,根结点保存分数最低的beam。

    def beam_search(self,
                    x,
                    max_sum_len,
                    beam_width,
                    len_oovs,
                    x_padding_masks):
        """Using beam search to generate summary.
        """
        # run body_sequence input through encoder
        encoder_output, encoder_states = self.model.encoder(
            replace_oovs(x, self.vocab))
        coverage_vector = torch.zeros((1, x.shape[1])).to(self.DEVICE)
        # initialize decoder states with encoder forward states
        decoder_states = self.model.reduce_state(encoder_states)
        # initialize the hypothesis with a class Beam instance.
        init_beam = Beam([self.vocab.SOS],
                         [0],
                         decoder_states,
                         coverage_vector)
        k = beam_width
        curr, completed = [init_beam], []
        # use beam search for max_sum_len (maximum length) steps
        for _ in range(max_sum_len):
            # get k best hypothesis when adding a new token
            topk = []
            for beam in curr:
                # When an EOS token is generated, add the hypo to the completed
                if beam.tokens[-1] == self.vocab.EOS:
                    completed.append(beam)
                    k -= 1
                    continue
                for can in self.best_k(beam,
                                       k,
                                       encoder_output,
                                       x_padding_masks,
                                       x,
                                       torch.max(len_oovs)
                                       ):
                    # Using topk as a heap to keep track of top k candidates.
                    add2heap(topk, (can.seq_score(), id(can), can), k)

            curr = [items[2] for items in topk]
            # stop when there are enough completed hypothesis
            if len(completed) == beam_width:
                break
        completed += curr
        # sort the hypothesis by normalized probability and choose the best one
        result = sorted(completed,
                        key=lambda x: x.seq_score(),
                        reverse=True)[0].tokens
        return result


5. Rouge评估

我们在本次项目中使用ROUGE-1、ROUGE-2以及ROUGE-L评估。ROUGE分数的我博客里也有详细介绍。首先我们建立自带的ROUGE容器初始化,然后预测函数得到我们生成对文本,将他们Build hypotheses,最后比较答案得到分数。

class RougeEval():
    def __init__(self, path):
        self.path = path
        self.scores = None
        self.rouge = Rouge()
        self.sources = []
        self.hypos = []
        self.refs = []
        self.process()
    def process(self):
        print('Reading from ', self.path)
        with open(self.path, 'r') as test:
            for line in test:
                source, ref = line.strip().split('')
                ref = ''.join(list(jieba.cut(ref))).replace('。', '.')
                self.sources.append(source)
                self.refs.append(ref)
        print(f'Test set contains {len(self.sources)} samples.')


    def build_hypos(self, predict):
        print('Building hypotheses.') 
        count = 0
        for source in self.sources:
            count += 1
            if count % 100 == 0:
                print(count)
            self.hypos.append(predict.predict(source.split()))

    def get_average(self):
        assert len(self.hypos) > 0, 'Build hypotheses first!'
        print('Calculating average rouge scores.')
        return self.rouge.get_scores(self.hypos, self.refs, avg=True)

    def one_sample(self, hypo, ref):
        return self.rouge.get_scores(hypo, ref)[0]
rouge_eval = RougeEval(config.test_data_path)
predict = Predict()
rouge_eval.build_hypos(predict)
result = rouge_eval.get_average()
print('rouge1: ', result['rouge-1'])
print('rouge2: ', result['rouge-2'])
print('rougeL: ', result['rouge-l'])



6. 数据增强

少样本问题是 NLP 领域经常面临的,尤其是在金融或者医疗等垂直领域,更是缺乏高质量的标注语料,所以数据增强是一种常用的技术。这一环节实现以下几种数据增强的技术:单词替换,回译,半监督学习。

6.1 单词替换

由于中文不像英文中有 WordNet 这种成熟的近义词词典可以使用,我们选择在embedding 的词向量空间中寻找语义最接近的词。通过使用在大量数据上预训练好的中文词向量,我们可以到每个词在该词向量空间中语义最接近的词,然后替换原始样本中的词,得到新的样本。但是有一个问题是,如果我们替换了样本中的核心词汇,比如将文案中的体现关键卖点的词给替换掉了,可能会导致核心语义的丢失。对此,我们有两种解决办法:1. 通过 tfidf 权重对词表里的词进行排序,然后替换排序靠后的词;2. 先通过无监督的方式挖掘样本中的主题词,然后只替换不属于主题词的词汇。

任务1,extract_keywords函数。
根据TFIDF确认需要排除的核⼼词汇。

    def extract_keywords(self, dct, tfidf, threshold=0.2, topk=5):
        """find high TFIDF socore keywords
        """
        tfidf = sorted(tfidf, key=lambda x: x[1], reverse=True)
        return list(islice(
            [dct[w] for w, score in tfidf if score > threshold], topk
            ))

任务2,replace函数。
embedding 的词向量空间中寻找语义最接近的词进⾏替换。

    def replace(self, token_list, doc):
        """replace token by another token which is similar in wordvector 
        """
        keywords = self.extract_keywords(self.dct, self.tfidf_model[doc])
        num = int(len(token_list) * 0.3)
        new_tokens = token_list.copy()
        while num == int(len(token_list) * 0.3):
            indexes = np.random.choice(len(token_list), num)
            for index in indexes:
                token = token_list[index]
                if isChinese(token) and token not in keywords and token in self.wv:
                    new_tokens[index] = self.wv.most_similar(
                        positive=token, negative=None, topn=1
                        )[0][0]
            num -= 1
        return ' '.join(new_tokens)

任务3,generate_samples函数。

    def generate_samples(self, write_path):
        """generate new samples file
        """
        replaced = []
        count = 0
        for sample, token_list, doc in zip(self.samples, self.refs, self.corpus):
            count += 1
            if count % 100 == 0:
                print(count)
                write_samples(replaced, write_path, 'a')
                replaced = []
            replaced.append(
                sample.split('')[0] + '  ' + self.replace(token_list, doc)
                )

替换全部的reference,和对应的source形成新样本

6.2 回译

我们可以使用成熟的机器翻译模型,将中文文本翻译成一种外文,然后再翻译回中文,由此可以得到语义近似的新样本。

利⽤百度translate API 接⼝将source ,reference翻译成英语,再由英语翻译成汉语,形成新样本。具体请参看官网。另外不同的语⾔样本的训练效果会有所不同,建议多尝试⼏种中间语⾔,⽐如⽇语等。

任务1:translate函数
建⽴http连接,发送翻译请求,以及接收翻译结果。关于这⼀部分可以参考百度接⼝translate API 的demo。

def translate(q, source, target):
    """translate q from source language to target language
    """
    #  refer to the official documentation   https://api.fanyi.baidu.com/  
    # There are demo on the website ,  register on the web site ,and get AppID, key, python3 demo.
    appid = ''  # Fill in your AppID
    secretKey = ''  # Fill in your key
    httpClient = None
    myurl = '/api/trans/vip/translate'
    fromLang = source  # The original language
    toLang = target  # The target language
    salt = random.randint(32768, 65536)
    sign = appid + q + str(salt) + secretKey
    sign = hashlib.md5(sign.encode()).hexdigest()
    myurl = '/api/trans/vip/translate' + '?appid=' + appid + '&q=' + urllib.parse.quote(
        q) + '&from=' + fromLang + '&to=' + toLang + '&salt=' + str(
        salt) + '&sign=' + sign
    try:
        httpClient = http.client.HTTPConnection('api.fanyi.baidu.com')
        httpClient.request('GET', myurl)
        # response is HTTPResponse object
        response = httpClient.getresponse()
        result_all = response.read().decode("utf-8")
        result = json.loads(result_all)
        return result
    except Exception as e:
        print(e)
    finally:
        if httpClient:
            httpClient.close()

任务2:back_translate函数
对数据进⾏回译。

def back_translate(q):
    """back_translate
    """
    en = translate(q, "zh", 'en')['trans_result'][0]['dst']
    time.sleep(1.5)
    target = translate(en, "en", 'zh')['trans_result'][0]['dst']
    time.sleep(1.5)
    return target

6.3 自助式样本生成

当我们训练出一个文本生成模型后,我们可以利用训练好的模型为我们原始样本中的 reference 生成新的 source,并作为新的样本继续训练我们的模型。

semi_supervised函数
我们可以使⽤训练好的PGN 模型将reference送⼊模型,⽣成新的source:

def semi_supervised(samples_path, write_path, beam_search):
    """use reference to predict source
    """
    pred = Predict()
    print('vocab_size: ', len(pred.vocab))
    # Randomly pick a sample in test set to predict.
    count = 0
    semi = []
    with open(samples_path, 'r') as f:
        for picked in f:
            count += 1
            source, ref = picked.strip().split('')
            prediction = pred.predict(ref.split(), beam_search=beam_search)
            semi.append(prediction + '  ' + ref)
            if count % 100 == 0:
                write_samples(semi, write_path, 'a')
                semi = []

下面给一个样例:
source:

帕莎 太阳镜 男⼠ 太阳镜 偏光 太阳眼镜 墨镜 潮 典雅 灰 颜⾊ 选择 , 细节 特⾊ 展示 , ⻆度 展示 , ⻛ 尚 演 , ⾦属, 透光 量 , 不易 变形 , 坚固耐⽤ , 清晰 柔和 , 镜⽚ 材质 , 尼⻰ ⾼ , 型号 , 镜架 材质 , ⾦属 镜腿 , 镜布 镜盒 , ⿐ 间距 , 轻盈 ⿐托 , 品牌 刻印 , ⽆缝 拼接 眼镜 配件类型 镜盒 功 能 偏光 类型 偏光太阳镜 镜⽚材质 树脂 ⻛格 休闲⻛ 上市时间 2016年夏季 镜框形状 ⽅形 适⽤性别 男 镜架材质 合⾦

reference:

夏天 到 了 , 在 刺眼 的 阳光 下 少不了 这 款 时尚 的 男⼠ 太阳镜 ! 时尚 的 版型 , 适⽤ 各种 脸型 , 突出 您 的 型 男 ⻛范 。 其⾼ 镍 ⾦属 材质 的 镜架 , ⼗分 的 轻盈 , 带给 您 舒适 的 佩戴 体验 。

将reference送⼊模型后⽣成的数据new source:

版型 设计 , 时尚 百搭 , 适合 多种 场合 佩戴 。 时尚 ⾦属 拉链 , 经久耐⽤ 。 带给 你 舒适 的 佩戴 体验 。 ⻛范 与 铰链 的 镜架 , 舒适 耐磨 , 不易 变形 。 带给 你 意想不到 的 修身 效果 , 让 你 的 夏 季 充满 着 不 ⼀样 的 魅⼒ , 让 这个 夏天 格外 绚烂 , 让 这个 夏天 格外 绚烂 。 时尚 的 版型 , 让 你 穿 起来 更 有 男⼈味 , 让 这个 夏天格外 绚烂 绚烂 , 让 这个 夏天 格外 绚烂 。 时尚 的 版型 , 让 你 穿 起来 更 有 男⼈味 , 让 这个 夏天 格外 绚烂 绚烂 , 是 秋装 上 上 之选 。



7. 优化技巧

在 Seq2seq 模型中,由于 Decoder 在预测阶段需要根据上一步的输出来的
生成当前 time step 的输出,所以会面临“Exposure bias”的问题:在训练
阶段我们使用 ground truth 作为 decoder 的输入(称之为 Teacher forcing),
预测阶段却只能使用 decoder 上一步的输出,导致输入样本分布不一样,而
影响 decoder 的表现。对此,我们有两种技巧进行优化:

7.1 Weight tying

即共享 Encoder 和 Decoder 的 embedding 权重矩阵,使得其输入的词向量表达具有一致性。使⽤的是three-way tying,即Encoder的input embedding,Decoder的input emdedding和Decoder的output embedding之间的权重共享。
encoder端中加入如下代码:

        if config.weight_tying:
            embedded = decoder_embedding(x)
        else:
            embedded = self.embedding(x)

Decoder端在output中加入如下代码:

    if config.weight_tying:
            FF2_out = torch.mm(FF1_out, torch.t(self.embedding.weight))
        else:
            FF2_out = self.W2(FF1_out)

7.2 Scheduled sampling

即在训练阶段,将 ground truth 和 decoder的输出混合起来使用作为下一个 time step 的 decoder 的输入,具体的做法是每个 time step 以一个 p p p 的概率进行 Teacher forcing,以 ( 1 − p ) (1-p) (1p)的概率不进行 Teacher forcing。 p p p 的大小可以随着 batch 或者 epoch衰减,即开始训练的阶段完全使用 groud truth 以加快模型收敛,到后面逐渐将 ground truth 替换成模型自己的输出,到训练后期就与预测阶段的输出一致了。

代码也很简单,如下所示:

实现ScheduledSampler即可,进入每个epoch都要去确定是否要进行teacher_forcing。可以通过epoch或者batch数控制按照⼀定的概率给出是否需要进⾏teacher forcing的指示。

class ScheduledSampler():
    def __init__(self, phases):
        self.phases = phases
        self.scheduled_probs = [i / (self.phases - 1) for i in range(self.phases)]

    def teacher_forcing(self, phase):
        """According to a certain probability to choose whether to execute teacher_forcing
        """
        sampling_prob = random.random()
        if sampling_prob >= self.scheduled_probs[phase]:
            return True
        else:
            return False

如果epoch=0,那么百分之百进行Teacher forcing,如果epoch=num_eopch-1,那么百分之零进行Teacher forcing。


8. 实验结果

下面实验结果大家可以作为一个参考,对于不同数据不同任务不同超参数可能会有差异,不过也大致反映了一些规律。如下表所示:

模型 ROUGE1 ROUGE2 ROUGEL
seqseq+att 0.230632 0.0407 0.13037
PGN 0.24565 0.042663 0.154976
PGN+converage 0.267554 0.04647 0.162138
PGN+Scheduled sampling 0.246721 0.04284 0.15831
PGN+weight_tying 0.248096 0.04334 0.15791
PGN+big_samples 0.25631 0.04995 0.16245

感觉优化效果最好的是Converage和数据增广。

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