The readers hope that I can combine the classical Chinese practices such as "Mortality" to show how the self-attention mechanism captures the "confrontation between the present and the past".
First, the reader previously pointed out that the key points were missing when summarizing the dot-shaped structure of the Dungu, and emphasized the need to criticize the Malukov hypothesis rather than a complete denial, and hoped to explain the Transformer in a game-based way.
Nowadays, writers use specific examples of classical Chinese, especially "Moral Book", to demonstrate the application of self-attention mechanisms when dealing with the structure of the countermeasures.
First, I need to determine the role of self-attention mechanism in Transformer, especially how to deal with long-distance dependencies and contextual relationships.
Then, select the classical confrontational sentences in "Mythology", such as "The present is also the present is the past", and analyze the syntactic structure and linguistic contradictions in it.
Next, we need to simulate how the self-attention mechanism calculates the attention weight between different words in these sentences, and then captures the relationship between "present" and "than", "visual" and "visual".
At the same time, considering the characteristics of classical Chinese mentioned by readers, such as omission, allusion nesting and nonlinear syntax, it is necessary to explain how the self-attention mechanism responds to these challenges, and traditional RNNs or LSTMs may have shortcomings.
For example, RNNs tend to forget the previous content when processing long sentences, while self-attention can process the entire sentence at one time and pay attention to all related words.
It is also necessary to design a specific example to gradually decompose the process of self-attention mechanism, including steps such as input embedding, position code, multi-head attention calculation, and use the sentences from "Morality" as cases. This way readers can understand abstract mechanisms through specific examples.
In addition, readers may want to understand the application of this method in modern NLP, such as the actual effect of digitalization of ancient books or translation of classical Chinese. Therefore, some application scenarios or case studies can be supplemented to illustrate how the self-attention mechanism improves the accuracy and efficiency of classical Chinese processing.
Finally, make sure that the entire explanation is clear and easy to understand, avoid technical language, and use genre and game-based methods to explain the understanding of beginners. Each step needs to be described in detail in words and explained in combination with specific examples of "Morality".
The author combines the classical Chinese practice of "My Morality" to explain in detail how the attention mechanism captures the process of "the current and the past". Disassemble and focus attention through specific example sentences to take you through the deep-level logic of Transformer.
"Let the people regain their veils and use them, and they will be very good at treating them, so they will be happy with their food, so they will be comfortable with their homes, and they will be happy with their customs. The people will face each other, and the chickens and dogs will not come together when they are old and die."
Mission: Capture the conflict between "cords and use them" and "never interact with each other when you are old and die"
• Contrary features:
o Verbal structure: "Tong" (movement) vs "Fighting with each other" (Address + word)
o Time and space conflict: "return" (return to the past) vs "not coming together" (separate from the present)
o Social imagery: original memory vs modern separation.
Step 1: Input processing of self-attention mechanism
1.1 Embedding layer
Convert classical Chinese lyrics to high-dimensional vectors:
• "Tie" → Vector A
• "Meeting each other" → Vector B
• "Recover" → Vector C
• "Don't come together" → Vector D
• Key design: Classical Chinese lexicon (such as "corners") will be given a different vector space than modern lexicon. For example, a vector of "return" will enhance the meaning of "retrogression".
1.2 Positional Encoding
Add timing coordinates to each word:
• "Connection" at the beginning of the sentence → Coordinate(0)
• "Blog" at the beginning of the sentence +1 → Coordinate(1)
• "Photo" in the sentence → Coordinate(10)
• Purpose: retain the distance information between "Gain" and "Gain" to avoid the machine misinterpreting "Gain" into a modern word.
Step 2: Self-Attention Calculation
Calculate the "attention" of each word to other words to capture the relationship.
2.1 Attention power and weight
Suppose the model calculates the following degree of attention (numerical range 0-1):
Words, conjunction, reunion, not to contact each other
Rope 0.8 0.2 0 0
Contact 0.3 0.7 0 0.6
Recover 0.1 0 0.9 0
No contact with each other 0 0.4 0 0.8
2.2 Explain the reading mechanism
• "Tong" → "Don't talk to each other":
Although the distance between the two words is farther (15 words), the attention of "return" (vector C) to "not indirectly" (vector D) is 0.8, implying that "return" is a time-space reversal, enhancing the opposition of the latter sentence.
• "Traveling with each other" → "Don't talk with each other":
The attentional power of direct contrast words ("phase" vs "unfamily") reaches 0.6, and the model automatically compares the affirmation and negation of the verb structure.
Step 3: Multi-Head Attention
Expand a single focus to multiple professional dimensions:
3.1 Cultural symbol head
• Focus on the original memory image of "cords", ignore the literal meaning of "cords" (such as "cords"), and strengthen its connection with "recovery".
3.2 Poetry and Song Confrontation Head
• Capture the syntax difference between "structure and use" (movement structure) and "never interact with each other" (structure in the state), and generate a counterpoint score (such as 0.85).
3.3 Time and space turn
• Analyze the "recovery" as a time adverb, how to introduce the "no contact" in the next sentence to establish a time-space alignment logic link.
Step 4: Output layer: Generate counter-analysis
Based on the attention weight, the model outputs the following structural analysis:
json
{
"Confrontation": [
{
"Previous sentence": "Clutch and use it",
"The next sentence": "Don't talk to each other when you are old and die",
"Confrontation type": "Time and space opposition",
"Keyword Embodiment": {
"Tong": "Don't come together",
"Return": "Not in line"
},
"Similarity": 0.82
}
],
"Cultural metaphor": "By denying the 'community' of modern society, we call the pure and simple style of the original trunk"
}
Critical reflection: a comparison with traditional training
- Breakthrough:
• Self-attention does not need to rely on tool books such as "Lya" and "Speaking the Writing and Interpretation of Words" to directly learn the metaphor of "cornering" from data.
• Break the limitations of "shape" (interpreting characters in fonts), such as the oracle pictogram of "going" (the goal of foot) being transformed into a vector feature of "movement link". - limitation:
• Cultural bias: If the training data lacks the Ethical Book’s specialty, the model may “cord” errors in response to modern verbs (such as “editors”).
• Too sensitive to the countermeasures: In "The Tao can be told the extraordinary way", the "talkable" and "extraordinary way" may be used to force the countermeasures, ignoring the differences in their philosophical levels.
Design classical Chinese counterpart practice platform - Game design:
• Use the Transformer model to actually rate students' adaptations of "Speech" and "Chu Yuan" (such as changing "Study and Time" to "Study and Time").
• Show attentional power heavy force diagrams to guide students to understand which vocabulary is at the heart of the match. - Case description:
• Students enter: "The best is like water, and water conservancy is full of things without competing"
• Model Reflection:
markdown
✅ Best match: "The best good is like water" → "The lower virtue is like earth" (self-attention weight 0.9)
❌ The second advantage: "The best is like fire" (the degree of resistance between "fire" and "water" is only 0.3)
Conclusion
The self-attention mechanism captures the morality of essentially combining mathematical optimization with humanistic meaning. It neither relies on classical annotations like traditional practice, nor does it neglect the context as coldly as the statistical model. Instead, it automatically learns what "appropriate aesthetics" is through billions of "silent writing" of classical Chinese sentences. This is where Transformer's revolutionary lies———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————