Data Augmentation Methods in TinyBERT

Data augmentation is a crucial technique employed in TinyBERT, particularly during the task-specific distillation phase. Its primary purpose is to generate additional data points that are used for fine-tuning the student model. This process ensures that the general TinyBERT model can be effectively adapted to specific Natural Language Processing (NLP) tasks, even when the amount of available labeled data is limited.

Step-by-Step Data Augmentation Algorithm

The following algorithm outlines the process of data augmentation in TinyBERT:

Input Sentence Example: "Paris is a beautiful city"

1. Tokenization:

   • Use a BERT tokenizer to split the input sentence into tokens.
   • Example: X = ["Paris", "is", "a", "beautiful", "city"]
   • Create a masked copy of the token list: X_masked = ["Paris", "is", "a", "beautiful", "city"]

2. Iterate Over Each Token (i):

   • If X[i] is a single-piece word (e.g., "Paris"):

     • Replace the token at X_masked[i] with the [MASK] token.
     • Example: X_masked = ["[MASK]", "is", "a", "beautiful", "city"]
     • Use BERT-Base to predict the top-K most likely words for the [MASK] token.
     • Store these predictions in a candidates list.
     • Example: If K=3, candidates = ["Paris", "it", "that"]

   • If X[i] is not a single-piece word (i.e., a subword token):

     • Use GloVe embeddings to find the K most semantically similar words to the subword token.
     • Store these similar words in the candidates list.

3. Sampling and Replacement:

   • Generate a random floating-point number r from a uniform distribution between 0 and 1 (r ~ Uniform[0, 1]).
   • Define a replacement probability threshold p (e.g., p = 0.5).
   • Decision:
     • If r ≤ p: Replace X_masked[i] with a randomly sampled word from the candidates list.
     • Otherwise (r > p): Retain the original word from X[i] at X_masked[i].

4. Data Collection:

   • Repeat steps 2 and 3 for all tokens in the sentence.
   • Add the modified X_masked sequence to the data_aug list.

5. Generate Multiple Augmented Samples:

   • Repeat the entire process (steps 1-4) N times (e.g., N=10) for each original sentence. This generates multiple augmented versions of the same sentence, effectively expanding the dataset.

Example: Augmenting a Sentence

Let's augment the sentence X = ["Paris", "is", "a", "beautiful", "city"] with K=3 and p=0.5.

Iteration 1:

• Token 0: "Paris"

  • X_masked = ["[MASK]", "is", "a", "beautiful", "city"]
  • BERT-Base predicts candidates = ["Paris", "it", "that"].
  • Suppose r = 0.3. Since r (0.3) ≤ p (0.5), we randomly sample from candidates. Let's say we pick "it".
  • The token is replaced: X_masked[0] becomes "it".
  • The augmented sequence is now ["it", "is", "a", "beautiful", "city"].
  • Add ["it", "is", "a", "beautiful", "city"] to data_aug.

• Token 1: "is"

  • Assume "is" is a single-piece word.
  • X_masked = ["it", "[MASK]", "a", "beautiful", "city"]
  • BERT-Base predicts candidates = ["is", "was", "are"].
  • Suppose r = 0.7. Since r (0.7) > p (0.5), the original word "is" is retained.
  • The sequence remains ["it", "is", "a", "beautiful", "city"].

• This process continues for all tokens.

The entire procedure is repeated N times for the original sentence to produce diverse augmented examples.

Fine-Tuning with the Augmented Dataset

The data_aug list, containing the synthetically generated data, is then used in conjunction with the original (potentially limited) labeled data. This combined dataset is used to fine-tune the general TinyBERT model. This process significantly enhances the model's performance and adaptability for specific downstream NLP tasks.

Key Highlights of TinyBERT

• Distillation at All Layers: TinyBERT leverages knowledge distillation across embedding, encoder, and prediction layers, not just the output layer.
• Two-Phase Learning: It follows a two-phase learning strategy: general pre-training followed by task-specific fine-tuning.
• Efficient and Lightweight:
  • Achieves approximately 96% of BERT's performance.
  • Is 7.5x smaller than BERT.
  • Offers 9.4x faster inference compared to BERT.

SEO Keywords

• TinyBERT Data Augmentation
• Task-Specific Distillation
• NLP Fine-tuning
• BERT Tokenization
• Masked Language Model (MLM) for Augmentation
• GloVe Embeddings for Augmentation
• Dataset Expansion
• General TinyBERT Adaptation

Interview Questions

• What is the primary objective of data augmentation in TinyBERT during the task-specific distillation phase?
• How does TinyBERT employ BERT-Base for augmenting "single-piece words" during the data augmentation process?
• When encountering a non-"single-piece word" (subword token), what method does TinyBERT use to identify candidate words for augmentation?
• Explain the "Sampling and Replacement" mechanism in the data augmentation algorithm, detailing the roles of the random value r and the threshold p.
• Why is the data augmentation process repeated multiple times (e.g., N times) for each sentence?
• How does the augmented dataset facilitate the fine-tuning of the general TinyBERT model?
• What is the primary advantage of data augmentation when labeled data for a specific NLP task is scarce?
• How does TinyBERT's "distillation at all layers" approach complement the data augmentation strategy beyond just the output layer?
• How does data augmentation contribute to TinyBERT's remarkable efficiency (96% performance with significantly reduced size)?
• When implementing this data augmentation strategy, what key considerations would you make regarding the selection of K (number of top candidates) and p (replacement probability)?