NLP-Projects

Natural Language Processing projects, which includes concepts and scripts about:

• 0_Word2vec

  • gensim, fastText and tensorflow implementations. See Chinese notes, 中文解读

• 1_Sentence2vec

  • doc2vec, word2vec averaging and Smooth Inverse Frequency implementations

• 2_Machine_reading_comprehension

• 3_Dialog_system

  • Categories and components of dialog system

• 4_Text_classification

  • tensorflow LSTM (See Chinese notes 1, 中文解读 1 and Chinese notes 2, 中文解读 2)
  • fastText implementation

• 5_Pretraining_LM

  • Principle of ELMo, ULMFit, GPT, BERT, XLNet

• 6_Sequence_labeling

  • Chinese_word_segmentation
    • HMM Viterbi implementations. See Chinese notes, 中文解读
  • Named_Entity_Recognition
    • Brands NER via bi-directional LSTM + CRF, tensorflow implementation. See Chinese notes, 中文解读

• 7_Information_retrieval

• 8_Information_extraction

• 9_Knowledge_graph

• 10_Text_generation

• 11_Network_embedding

Concepts

1. Attention

• Attention == weighted averages
• The attention review 1 and review 2 summarize attention mechanism into several types:
  • Additive vs Multiplicative attention
  • Self attention
  • Soft vs Hard attention
  • Global vs Local attention

2. CNNs, RNNs and Transformer

• Parallelization [1]

  • RNNs
    • Why not good ?
    • Last step's output is input of current step
  • Solutions
    • Simple Recurrent Units (SRU)
      • Perform parallelization on each hidden state neuron independently
    • Sliced RNNs
      • Separate sequences into windows, use RNNs in each window, use another RNNs above windows
      • Same as CNNs
  • CNNs
    • Why good ?
    • For different windows in one filter
    • For different filters

• Long-range dependency [1]

  • CNNs
    • Why not good ?
    • Single convolution can only caputure window-range dependency
  • Solutions
    • Dilated CNNs
    • Deep CNNs
      • N * [Convolution + skip-connection]
      • For example, window size=3 and sliding step=1, second convolution can cover 5 words (i.e., 1-2-3, 2-3-4, 3-4-5)
  • Transformer > RNNs > CNNs

• Position [1]

  • CNNs

    • Why not good ?
    • Convolution preserves relative-order information, but max-pooling discards them

  • Solutions

    • Discard max-pooling, use deep CNNs with skip-connections instead
    • Add position embedding, just like in ConvS2S

  • Transformer

    • Why not good ?
    • In self-attention, one word attends to other words and generate the summarization vector without relative position information

• Semantic features extraction [2]

  • Transformer > CNNs == RNNs

3. Pattern of DL in NLP models [3]

• Data

  • Preprocess
    • Sub-word segmentation to avoid OOV and reduce vocabulary size
      • sentencepiece
  • Pre-training (e.g., ELMO, BERT)
  • Multi-task learning
  • Transfer learning, ref_1, ref_2
    • Use source task/domain S to increase target task/domain T
  • If S has a zero/one/few instances, we call it zero-shot, one-shot, few-shot learning, respectively

• Model

  • Encoder
    • CNNs, RNNs, Transformer
  • Structure
    • Sequential, Tree, Graph

• Learning (change loss definition)

  • Adversarial learning
  • Reinforcement learning

References

• [1] Review
• [2] Why self-attention? A targeted evaluation of neural machine translation architectures
• [3] ACL 2019 oral

Awesome public apis

• Baidu AI Open Platform
• Tencent AI Open Platform
• Tencent NLP

Awesome packages

Chinese

• pyltp
• HanLP

English

• Spacy
• gensim
• Install tensorflow with one line: conda install tensorflow-gpu

Future directions

• Multi-task learning
• Self-training