Note 2: ELMo

Deep contextualized word representations

Peters et al, 2018

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1. ELMo (Embeddings from Language Models) learns a linear combination of the vectors stacked above each input word for each end task, which markedly improves performance over just using the top LSTM layer.
   • High-level captures the context-dependent aspects of word meaning.
   • Low-level captures the basic syntax.

   • Different from others, ELMo word representations are functions of the entire input sentence.

     
     
     [Devlin et al. 2019]

2. Bidirectional language models (biLM)

Given a sequence of tokens ,

• A forward language model computes the probability of the sequence by modeling the probability of token given the history :
  
• A backward language model computes the probability of the token given the future context :
  
• A biLM combines both a forward and backward LM and jointly maximizes the log likelihoods of both directions:
  
  where the forward and backward LMs share the parameters in token representation layer and Softmax layer , except their LSTMs .

3. ELMO

• ELMo is a task specific combination of the intermediate layer representations in the biLM.
• For each token , a L-layer biLM computes a set of 2L+1 representations
  
  • is the token layer.
  • contains two outputs from the -th forward and backward BiLSTM layer at the position .
• In practice, ELMo has to collapse all layers in into a single vector.
  • Simply, ELMo just selects the top layer .
  • Generally, ELMo computes a task specific weighting of all biLM layers:
    
    where are softmax-normalized weights and the scalar parameter allows the task model to scale the tire ELMo vector.

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Reference

Peters, M. E., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K., & Zettlemoyer, L. (2018). Deep contextualized word representations. arXiv preprint arXiv:1802.05365.
Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.