Text-Generation

Challenges in Japanese Text Generation

When working on Japanese text summarization, title generation, and document classification tasks, do you face these problems?

1. Accuracy Issues

• Traditional rule-based methods cannot generate natural Japanese text
• English-oriented models cannot handle Japanese grammar and expressions
• Need to build separate models for multiple tasks

2. Development Cost Issues

• Time and resources required for task-specific model development
• Different approaches needed for document classification, summarization, and title generation
• Enormous effort required for preparing training data and building models

3. Operational Complexity

• Need to manage and operate multiple models
• Different APIs and interfaces for each task
• Complex model updates and maintenance

Real-world Text Generation Challenge Cases

Failure Case: Limitations of Task-specific Individual Development

# Traditional approach
classification_model = load_bert_classifier()      # For document classification
summarization_model = load_summarization_model()   # For summarization
title_generation_model = load_title_model()        # For title generation

# Problems:
# - Managing 3 separate models
# - 3x memory usage
# - High development and maintenance costs

The solution to this problem is Japanese T5 (Text-To-Text Transfer Transformer).