Transformer models like BERT, GPT, and their multilingual and multimodal descendants have demonstrated the remarkable ability to encode both syntactic and semantic information. But how do these models internally distinguish what something means from how it's structured? This post explores where and how that separation emerges in a transformer network, drawing parallels to multimodal and multilingual architectures.

Semantic Meaning vs. Syntactic Structure

In natural language, syntax governs how words are arranged (subject-verb-object, agreement, etc.), while semantics relates to the actual meaning. In transformers, both types of information are encoded into token embeddings and passed through multiple layers of self-attention.

What’s fascinating is that syntax and semantics naturally separate across the architecture—even though the model is never explicitly told to do so.

Attention Heads: Emergent Specialization

A standard transformer has multiple attention heads per layer, and each head computes an attention score:

Where $Q$ (Query), $K$ (Key), and $V$ (Value) are projections of the input. Different heads specialize in different roles:

• Syntactic heads attend to nearby tokens, identifying phrase structure and grammatical dependencies.
• Semantic heads attend to more distant but meaning-related tokens—often aligning words that are synonyms, references, or thematically related.

Example: English Sentence

The doctor who treated the patient smiled.

• Syntactic heads connect "who" to "doctor" and "treated" to "patient".
• Semantic heads might link "doctor" and "smiled" as part of a narrative event, even though they're far apart.

Layer Depth and Representational Hierarchy

Empirical studies (e.g., Tenney et al., 2019) show that transformer layers form a hierarchical representation:

• Lower layers: Capture syntax, part-of-speech tags, short-range dependencies.
• Middle layers: Encode dependency relations and phrasal meaning.
• Upper layers: Encode sentence-level meaning, coreference, and task-specific abstractions.

This makes intuitive sense: early layers handle local structure; deeper layers abstract away from surface form toward semantics.

Cross-lingual and Multimodal Alignment

This syntactic-semantic split also shows up in multilingual and multimodal models, where different languages (or modalities) are projected into a shared latent space. Examples include:

• mBERT and XLM-R for multiple languages
• CLIP and Flamingo for vision-language pairs
• Whisper and SpeechT5 for speech-text alignment

The mapping functions $f_{\text{text}}$ and $f_{\text{speech}}$ are trained to produce embeddings:

These projections are lossy—modality-specific nuances (e.g., accent in speech or stylistic tone in text) are often discarded in favor of shared semantics.

Analyzing Attention: Syntax or Semantics?

You can infer whether a head encodes syntax or semantics by analyzing its Q→K distances and alignment patterns:

• Short-range attention (head attends mostly to neighbors): likely syntactic
• Long-range attention to non-contiguous concepts: likely semantic

Example: Translation (Multilingual Setting)

Can We Filter by Meaning?

While not explicitly built into most transformers, post-hoc attribution analysis, head ablation, or probing classifiers can reveal head roles. Some models even introduce auxiliary losses to encourage head specialization.

Research Techniques:

• Ablation: Remove a head and measure performance drop
• Probing: Train a classifier on hidden states for linguistic properties
• Attribution: Use attention scores to assign meaning

Conclusion: Emergence, Not Design

The separation of syntax and semantics is not programmed—it emerges from gradient descent, architecture depth, and training data. Heads and layers specialize organically to support both linguistic structure and meaning extraction.

This property makes transformers effective across:

• Language translation
• Speech-to-text alignment
• Multimodal reasoning

…and reinforces the elegance of learning representations not by rules, but by attention.