NLP 入门知识点

最近从 B 站上找了个教程 学习NLP 的知识，就以此篇博客作为载体记录课上学的知识点吧。

Long Short Term Memory (LSTM) 模型

• LSTM uses a “conveyor belt” to get longer memory than SimpleRNN.

• Each of the following blocks has a parameter matrix:

  • Forget gate
  • Input gate
  • New values
  • Output values

• Number of parameters: 4 × times × shape(h) × times × [shape(h) + shape(x)]
  

Making RNNs More Effective

• SimpleRNN and LSTM are two kinds of RNNs; always use LSTM instead of SimpleRNN.
• Use Bi-RNN instead of RNN whenever possible.
• Stacked RNN may be better than a single RNN layer (if n is big).
• Pretrain the embedding layer (if n is small).

Stacked RNN

Bidirectional RNN

Text Generation (自动文本生成) Train a Neural Network

1. Partition text to (segment, next_char) pairs.
2. One-hot encode the characters.
   • Character → rightarrow → v v v × times × 1 vector.
   • Segment → rightarrow → l l l × times × v v v matrix
3. Build and train a neural network
   • l l l × times × v v v matrix ⇒ Rightarrow ⇒ LSTM ⇒ Rightarrow ⇒ Dense ⇒ Rightarrow ⇒ v v v × times × 1 vector

Text Generation

1. Propose a seed segment
2. Repeat the followings:
   a) Feed the segment (with one-hot) to the neural network.
   b) The neural network outputs probabilities.
   c) next_char ← leftarrow ← Sample from the probabilities.
   d) Append next_char to the segment.

机器翻译与 Seq2Seq 模型

如何提升 Seq2Seq

1. Bi-LSTM instead of LSTM (Encoder only!!!)
   • Encoder’s final states ( h t h_t ht​ and c t c_t ct​) have all the information of the English sentense.
   • If the sentence is long, the final states have forgotten early inputs.
   • Bi-LSTM (left-to-right and right-to-left) has longer memory.
   • Use Bi-LSTM in the encoder; use unidirectional LSTM in the decoder.
2. Word-Level Tokenization
   • Word-Level tokenization instead of char-level.
     • The average length of English words is 4.5 letters.
     • The sequences will be 4.5x shorter.
     • Shorter sequence -> less likely to forget.
   • But you will need a large dataset,
     • of (frequently used) chars is ~ 1 0 2 10^2 102 → rightarrow → one-hot suffices.
     • of (frequently used) words is ~ 1 0 4 10^4 104 → rightarrow → must be embedding.
     • Embedding layer has many parameters → rightarrow → overfitting!
   3. Multi-Task Learning (这样一来 encoder 只有一个而训练数据多了一倍，所以可以训练的更好)
      

Attention (注意力机制)

Seq2Seq 模型有个缺点是无法记住一个很长的句子的完整信息，所以有可能句子中有个别词被忘记而 decoder 无从得知完整的句子进入无法得到正确的翻译。

因此，一些研究者引入了 Attention 机制，Attention 的特点如下：

• Attention tremendously improves Seq2Seq model.
• With attention, Seq2Seq model does not forget source input.
• With attention, the decoder knows where to focus.
• Downside: much more computation
  

Transformer and BERT

1. Transformer is Seq2Seq modell; it has an encoder and decoder.
2. Transformer model is not RNN.
3. Transformer is based on attention and self-attention.
4. BERT is for pre-training Transformer’s encoder.