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).
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