Using Embedding Models to Predict Sentence Complexity (rewire.it)

Researchers are moving beyond century-old surface metrics like Flesch‑Kincaid toward using sentence embeddings to predict absolute sentence complexity—an objective, structure-focused notion of how linguistically intricate a sentence is. The work reframes complexity along four axes (syntactic, semantic, lexical, content) and shows why simple proxies (word length, sentence length) fail: they miss deep structure (parse-tree depth, branching, Yngve scores), semantic density, and domain-specific vocabulary. Parsing-based syntactic metrics are informative but computationally costly and limited to grammar; embeddings offer a compact, learnable alternative that can encode meaning and structure in high-dimensional geometry. Technically, the story traces evolution from Word2Vec (context‑independent) to BERT (bidirectional, pre-trained with Masked Language Modeling and Next Sentence Prediction) and then to SBERT, which reworks BERT into a Siamese encoder fine-tuned on NLI datasets with a contrastive loss so cosine distance correlates with semantic similarity. Practical details: off-the-shelf BERT token pooling often underperforms; SBERT-style fine-tuning (e.g., all‑MiniLM‑L6‑v2) plus L2 normalization produces embeddings whose cosine similarities cluster sentences by meaning and, importantly, signal complexity-relevant distinctions. The implication: with proper fine-tuning and task-specific supervision, embedding geometry can serve as a scalable, robust proxy for sentence complexity—enabling better readability measures, text simplification, and adaptive education tools—while still requiring careful calibration against syntactic and content-specific metrics.