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Multi-Tokenizer Approach 2: Light Changes to the Model Architecture

System Overview

This approach aims to improve tokenization across multiple languages while minimizing changes to the model architecture. It uses a shared vocabulary for all languages, with additional language-specific tokens.

graph LR
    A[Input Text] --> B[Language Detection]
    B --> C[UTF-8 Encoding]
    C --> D[Tokenization]
    D --> E[Token Sequence Generation]
    E --> F[Add Language-Specific Token]
    F --> G[Final Token Sequence]
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System Components

  1. Language Detection Module: Identifies the language of the input text.
  2. UTF-8 Encoder: Converts text to UTF-8 byte sequences.
  3. Tokenizer: Processes UTF-8 sequences into tokens.
  4. Language-Specific Token Adder: Prepends language identifier tokens.

Tokenization Process

sequenceDiagram
    participant User
    participant LD as Language Detector
    participant UTF8 as UTF-8 Encoder
    participant TK as Tokenizer
    participant LST as Language-Specific Token Adder

    User->>LD: Input Text
    LD->>UTF8: Detected Language & Text
    UTF8->>TK: UTF-8 Encoded Sequence
    TK->>LST: Token Sequence
    LST->>User: Final Token Sequence
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Vocabulary Design

The vocabulary consists of following tokens additional to the default latin based tokens:

  1. UTF-8 Byte Sequence Start specifier: b\xe6, b\xe7, etc. Helps the tokenizer identify the start of a new UTF-8 sequence.
  2. UTF-8 byte tokens (0-255): 256 tokens: \x00, \x01, ..., \xff
  3. Subword tokens: Most common byte sequences: \xe6\x88, \xe5\x96, b\xe8\x87, etc.
  4. Language-specific tokens: <EN>, <ES>, <CH>,</EN>, </ES>, </CH> etc.

Total additional tokens: 256 (byte tokens) + ~1000 (subword tokens) + ~10 (language tokens) = 1266

Tokenization Examples

Assume the input text is I love NLP. = 我喜欢自然语言处理. = Me encanta NLP.

English Output:

<EN><I>< love>< N><L><P><.></EN>

Chinese Output:

<CH><b\xe6><\x88><\x91><b\xe5><\x96\x9c><b\xe6><\xac><\xa2><b\xe8><\x87\xaa><b\xe7><\x84><\xb6><b\xe8\xaf><\xad><b\xe8><\xa8><\x80><b\xe5\xa4><\x84><b\xe7><\x90><\x86><.></CH>

Spanish Output:

<ES><Me>< enc><anta>< N><L><P><.></ES>

Advantages

  1. Minimal increase in vocabulary size
  2. Unified tokenization approach across languages
  3. Efficient handling of multi-language inputs
  4. Reduced model complexity compared to Approach 1

Challenges and Considerations

  1. Potential loss of language-specific nuances in tokenization
  2. Increased reliance on the model to learn language-specific patterns
  3. Possible increase in sequence length for non-Latin script languages

Implementation Steps

  1. Develop a robust language detection module
  2. Create a UTF-8 encoding and tokenization pipeline
  3. Generate a shared vocabulary based on a multi-language corpus
  4. Implement the language-specific token addition mechanism
  5. Integrate the system with the existing model architecture
  6. Fine-tune the model on multi-language data using the new tokenization approach

This approach offers a balance between improved multi-language support and minimal changes to the model architecture, potentially leading to better performance across various languages without significantly increasing model complexity.