Contextual Models: BERT and Beyond

Updated 19 October 2025

Contextual models such as BERT are deep learning architectures that generate token-level representations based on surrounding context, enabling nuanced language understanding.
They utilize Transformer networks, masked language modeling, and self-attention to create dynamic embeddings that outperform static methods like Word2Vec.
Adaptations such as Clinical BERT and mBERT highlight their versatility, achieving measurable improvements in domain-specific and multilingual NLP benchmarks.

Contextual models such as BERT represent a pivotal advancement in natural language processing, providing dynamic word or sentence-level representations that are a function of the surrounding linguistic context. Unlike previous static embeddings, which assign a single global vector to each word type, contextual models leverage architectural innovations—primarily deep recurrent and Transformer networks and associated pretraining objectives—to produce context-sensitive representations for tokens, phrases, and sentences. These models have demonstrated substantial improvements on a broad spectrum of downstream NLP tasks, have prompted analyses of their internal properties and limitations, and have motivated the development of domain-specific, multilingual, and task-tailored variants.

1. Architectural Foundations and Core Mechanisms

Contextual models assign representations dynamically at inference time by conditioning each token’s encoding on its complete surrounding sequence, as opposed to static methods such as Word2Vec or GloVe. Architectures include bidirectional LSTMs (e.g., ELMo), unidirectional Transformers (e.g., GPT), and bidirectional Transformers exemplified by BERT. The fundamental formulation in BERT is to encode an input sequence as:

$E(\mathbf{x}) = \text{BERT}([\text{CLS}], x_1, ..., x_n, [\text{SEP}])$

where $E$ is a stack of Transformer layers applying multi-head self-attention and feed-forward operations. Each output $h_{i,l}$ is a function of all tokens in the sequence up to layer $l$ . BERT and its descendants are pretrained via masked language modeling (MLM), in which some tokens are replaced with a [MASK] symbol and the model predicts those tokens using both left and right context. Contextual models may also incorporate objectives such as next-sentence prediction (NSP) or, in multilingual settings, employ joint or aligned vocabulary strategies for cross-lingual transfer (Liu et al., 2020).

Unlike static embeddings $e_w$ in $\mathbb{R}^d$ , contextual embeddings are mathematically described as $h_w = f(\text{sequence}, w)$ , with $h_w$ depending on both the word $w$ and its context in the input sequence.

2. Linguistic Properties, Internal Geometry, and Representation Analysis

Contextual models induce highly anisotropic embedding spaces, with representations often occupying a narrow cone in the vector space (Ethayarajh, 2019). In BERT, the degree of context-specificity increases in higher layers, as measured by self-similarity and maximum explainable variance (MEV): less than 5% of the variance in a word’s contextual representations can be attributed to a single static direction. Thus, static compressions are inadequate approximations for true contextual encodings.

Probing studies (“edge probing” frameworks (Tenney et al., 2019)) reveal that contextual models robustly encode syntactic structure and sentence-level relations, significantly outperforming non-contextual baselines in syntactic phenomena (e.g., part-of-speech, dependency labeling). Performance gains on semantic phenomena (coreference, semantic role labeling, and proto-role labeling) are more modest, but deeper models such as BERT-large offer improvements for tasks dependent on long-range dependencies or non-local inference.

Furthermore, context-specificity is not uniformly distributed; in BERT, intra-sentence token representations grow more dissimilar in upper layers, supporting fine-grained disambiguation essential for tasks such as question-answering and coreference. Comparison with models such as GPT-2 and ELMo shows distinct behaviors in how context is exploited: BERT’s architecture balances individualized token representations with some sentence-level coherence, whereas GPT-2 exhibits extreme contextual differentiation for tokens even within the same sentence (Ethayarajh, 2019).

3. Supervised, Domain-specific, and Cross-lingual Adaptation

Contextual models can be further specialized through domain- or task-specific pretraining/fine-tuning. Clinical BERT models trained on the MIMIC-III clinical corpus—either on heterogeneous clinical notes or on discharge summaries—exemplify the performance benefits of domain-targeted adaptation: on the MedNLI clinical inference task, a domain-specific BERT achieves $82.7\%$ accuracy compared to $77.6\%$ for a vanilla model, setting new state-of-the-art results without architectural modification (Alsentzer et al., 2019). However, such adaptation is sensitive to dataset alignment. In de-identification tasks where the pretraining corpus uses uniform sentinel tokens for PHI but evaluation data employ synthetic randomized PHI, the performance advantage disappears, underscoring the necessity of matching pretraining and target data distributions.

Multilingual and cross-lingual applications rely on hybrid vocabulary learning (e.g., mBERT and XLM-R), trilingual or monolingual training for less-resourced languages, or post-hoc alignment. Empirical evidence confirms that monolingual or small-group (trilingual) BERT models trained on related languages outperform massive multilingual models in most labeling tasks (NER, POS), with cross-lingual gaps in macro F $_1$ as low as $5\%$ for NER and $8\%$ for POS tagging (Ulčar et al., 2021). For structural prediction (dependency parsing), L-ELMo remains competitive, suggesting architectural diversity remains valuable for certain syntactic phenomena.

4. Specialized Architectures and Knowledge Integration

Extensions to base architectures include explicit conditioning, structured knowledge infusion, and semantic supervision:

SemBERT incorporates external semantic role labels, fusing them with BERT’s subword representations through a convolutional, BiGRU, and concatenation pathway. This yields improved performance on NLU tasks such as SQuAD 2.0 and SNLI, establishing new state-of-the-art metrics and verifying that explicit structured semantics enhance inference and reading comprehension (Zhang et al., 2019).
Knowledge-Infused BERT (KI-BERT) augments the model with embeddings from knowledge graphs (ConceptNet, WordNet) projected into BERT's vector space, assigns unique token types and positions to entities, and enforces selective attention between tokens and entities. This approach boosts GLUE scores and outperforms BERT-large on domain-specific tasks with fewer parameters, confirming that the fusion of symbolic and contextual knowledge is beneficial when appropriately aligned and regularized (Faldu et al., 2021).
Conditioned contextual models (Contextual BERT, [GS]/[GSU] methods) integrate non-linguistic context (e.g., customer profiles) into each layer using an explicit global state which can be fixed or updated through the network. Experimental results show up to +43% relative improvements in recall for personalized prediction tasks compared to naive concatenation of context vectors (Denk et al., 2020).

5. Applications in Downstream Tasks

Contextual models have produced leading results in diverse tasks:

Text and Speech Processing: For ASR N-best reranking, BERT-based models (PBERT, TPBERT) outperform LSTM and MBERT-PLL by reframing reranking as a prediction problem, supplementing contextualized embeddings with unsupervised topic vectors to reduce WER (Chiu et al., 2021).
Information Retrieval: Contextual models provide significant nDCG@20 gains (+20%) over bag-of-words and static embedding baselines for natural language queries. Adaptation with search logs enables improved ranking even with limited training data, reflecting both syntactic and global query-document structure (Dai et al., 2019).
Sequential Sentence Classification: Inputting the entire document as a flattened sequence and extracting [SEP] representations for per-sentence classification outperforms hierarchical and CRF-based systems, achieving micro F1 = 92.9 on PubMed-rct (Cohan et al., 2019).
Error Detection in Clinical Domains: Conditional classification of prescriptions in context—using BERT/BioBERT and patient metadata—achieves up to 96.63% accuracy for text and 79.55% for speech input, providing practical value in EHR safety monitoring (Jiang et al., 2022).
Semantic Search and Similarity: Fine-tuned SBERT variants overcome the anisotropy problem of base BERT for clustering semantically similar questions, resulting in better separation and precision, as opposed to direct use of BERT or GPT-2 sentence embeddings (Zhu et al., 2022).
Knowledge Graph Completion: CAB-KGC leverages the neighborhood context (previous relations and neighboring entities for heads and all associated entities for relations), yielding a 5.3% Hit@1 improvement on FB15k-237 and 4.88% on WN18RR over SOTA approaches, and eliminating the need for entity descriptions and negative triplet sampling (Gul et al., 15 Dec 2024).

6. Model Compression, Efficiency, and Practical Deployment

Due to large model sizes, contextual models are frequently compressed via low-rank approximation, knowledge distillation, and quantization. For example, ALBERT implements factorized embedding parameterization; DistilBERT and TinyBERT transfer knowledge via supervised losses; Q-BERT uses Hessian-based mixed-precision quantization (Liu et al., 2020). In high-throughput settings (such as open-domain QA), decoupling contextual encoding—encoding questions and documents independently (DC-BERT)—achieves over 10x speed-up with minimal loss (<2%) in retrieval accuracy, opening paths for modularization and scaling (Zhang et al., 2020).

7. Analysis, Limitations, and Open Challenges

While contextual models robustly encode syntax and certain semantics, artifacts arise from architectural choices: BERT's segment embedding leaves measurable traces, introducing positional biases affecting tasks like semantic textual similarity when comparing across sentences; these biases reduce semantic coherence and can lead to suboptimal matching unless input handling is carefully controlled (Mickus et al., 2019).

Efforts to render embeddings more interpretable—such as mapping BERT representations to Binder’s 65-dimensional semantic feature space via supervised regression—highlight that much contextual information is recoverable and that the most discriminative semantic features are variably localized across model layers (Turton et al., 2020). Ongoing debates persist about which architectural and training decisions most effectively capture the linguistic abstractions (and which probe-extracted properties reflect genuine knowledge or mere separability).

Future advances will likely involve optimizing pretraining objectives, improving robustness to adversarial triggers, enabling finer-grained control over generated text, and developing resource-efficient fine-tuning and domain adaptation methods. Increasing focus is also directed toward applications for less-resourced and historical languages, where the careful alignment of training data to deployment context (chronologically and stylistically) is crucial for linguistic fidelity (Bamman et al., 2020, Cuscito et al., 7 Feb 2024).

Contextual models, most notably BERT and its derivatives, have reshaped language processing by providing rich, token- and context-dependent representations that surpass previous approaches in accuracy and flexibility. Ongoing research continues to probe their internal mechanisms, adapt them to specialized domains and languages, develop efficient deployment strategies, and address theoretical and practical limitations. These advances sustain contextual models as central components across both research and applied NLP.