[AI from Scratch] Episode 268: Challenges Unique to Japanese NLP

Recap and Today’s Theme

Hello! In the previous episode, we discussed the challenges and limitations of NLP, such as ambiguity, context understanding difficulties, and the lack of world knowledge in models.

Today, we will focus on the unique challenges of Japanese NLP. Japanese has several structural and orthographic features that distinguish it from other languages, presenting unique difficulties in natural language processing (NLP). This episode explores these challenges and suggests approaches to address them.

The Complexity of Japanese NLP

1. Structural Differences in the Language

Japanese has structural characteristics that differ significantly from other languages like English:

• High Flexibility in Word Order: In Japanese, word order is relatively flexible. The positions of the subject, predicate, and object can change depending on the context, making syntactic parsing more complex.
• Use of Particles: Particles such as “は” (wa), “が” (ga), and “を” (wo) are used to indicate the structure and meaning of a sentence. The interpretation changes depending on which particle is used.
• Frequent Omissions: In Japanese, the subject and object are often omitted, requiring the system to infer these elements from the context.

2. Complexity of the Writing System

Japanese combines three writing systems: Kanji, Hiragana, and Katakana. Additionally, it frequently incorporates alphabetic characters and numbers. This mix increases the complexity of text processing:

• Polysemy of Kanji: The same Kanji character can have multiple readings and meanings, requiring contextual interpretation.
• Kana Mixed Text: Japanese sentences typically contain both Kanji and Kana (Hiragana and Katakana), complicating tokenization (word segmentation).
• Katakana for Loanwords: Many foreign words are written in Katakana, but their pronunciation or meaning might differ from the original language.

3. Challenges in Tokenization

Unlike English, Japanese does not use spaces between words, making tokenization essential. Tokenization, however, faces several challenges:

• Ambiguous Word Boundaries: The same sequence of characters may have multiple meanings, leading to different segmentation methods depending on the meaning.
• Compound Words: Handling compound words like “機械学習” (machine learning) poses challenges, as segmentation decisions directly impact model accuracy.

4. Differences in Politeness and Style

Japanese features various forms of politeness and formality, including keigo (honorific language), which affect the nuances and meaning of text:

• Interpreting Honorifics: Honorifics require understanding the relationship and context between the speaker and the listener.
• Differences Between Polite and Casual Speech: Japanese can switch between polite and casual expressions, complicating context understanding.

Specific Issues in Japanese NLP

1. Tokenization (Word Segmentation)

Tokenization is particularly important in Japanese since words are not separated by spaces. Challenges include:

• Accuracy of Morphological Analysis: Tools like MeCab, Juman, and Sudachi perform morphological analysis, but they may not handle complex sentences or new words effectively.
• Handling New Words and Proper Nouns: New terms, names, and locations frequently appear, and if these are not in the dictionary, the system might fail to segment them correctly.

2. Polysemy in Kanji

Japanese Kanji often have multiple readings and meanings:

• Uncertainty in Kanji Reading: The same Kanji character may have different readings based on context, making it difficult to predict the correct reading (e.g., “生” can be read as “いきる” (ikiru), “なま” (nama), or “せい” (sei)).
• Ambiguity in Meaning: The meaning of Kanji often depends on the context, making it challenging to correctly interpret polysemous words.

3. Contextual Omissions

Japanese frequently omits subjects and objects, necessitating contextual understanding:

• Subject and Object Omission: Understanding the omitted elements requires inferring information from the surrounding sentences.
• Use of Honorifics: Determining who or what is referred to often depends on the use of honorifics, which requires careful interpretation.

4. Subword-Level Tokenization

Japanese words often consist of multiple components, making subword-level tokenization effective:

• Methods like Byte Pair Encoding (BPE) and WordPiece divide words into subword units, allowing flexible handling of new and unknown words.

Approaches to Improve Japanese NLP

1. Using Tokenization Tools

Morphological analysis tools are commonly used for Japanese tokenization. Some popular tools include:

• MeCab: A fast, accurate morphological analyzer that can be customized with dictionaries to handle new words and proper nouns.
• Sudachi: Offers multiple segmentation options, enabling both fine-grained and coarse-grained analysis.
• Juman++: Known for its high-accuracy analysis of complex Japanese sentences.

2. Fine-Tuning Language Models

Fine-tuning Japanese-specific language models (e.g., Japanese BERT or Japanese T5) enhances contextual understanding:

• Pre-training with Large Japanese Corpora: Retraining models on large Japanese text corpora adapts them to understand nuances and context more effectively.
• Domain-Specific Fine-Tuning: Fine-tuning with domain-specific corpora (e.g., medical, legal, financial) improves model performance in specialized fields.

3. Utilizing Subword Tokenization

Subword tokenization methods like WordPiece and BPE, used in models like BERT and GPT, are effective for handling the flexibility of Japanese expressions:

• Handling New and Compound Words: Subword tokenization allows the model to process new and compound words effectively by breaking them into manageable units.
• Dealing with Unknown Words: Even if the model encounters an unfamiliar word, breaking it into subwords allows it to partially grasp its meaning.

Summary

This episode focused on the unique challenges of Japanese NLP, including its flexible structure, complex writing system, and tokenization difficulties. Addressing these issues requires various approaches, such as using morphological analysis tools, fine-tuning language models, and utilizing subword tokenization.

Next Episode Preview

Next time, we will explore the latest trends in NLP, focusing on the evolution of large language models and their impact.

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Notes

1. Morphological Analysis: The process of dividing Japanese text into words and identifying their parts of speech.
2. Subword Tokenization: Dividing words into smaller subword units to handle unknown words flexibly.
3. Fine-Tuning: Retraining a pre-trained model for a specific task.