A SEMI-SUPERVISED FRAMEWORK NAMED AUGSBERT-UZ FOR HIGH-PERFORMANCE SEMANTIC TEXTUAL SIMILARITY IN UZBEK

SCIENCE AND INNOVATION INTERNATIONAL SCIENTIFIC JOURNAL VOLUME 4 ISSUE 11 NOVEMBER 2025 ISSN: 2181-3337 | SCIENTISTS.UZ 34 A SEMI-SUPERVISED FRAMEWORK NAMED AUGSBERT-UZ FOR HIGH-PERFORMANCE SEMANTIC TEXTUAL SIMILARITY IN UZBEK B.B. Muminov1, N.M. Allaberganova2 DSc., Professor, Tashkent State University of Economics, Tashkent, Uzbekistan1 PhD student, Tashkent University of Information and Technologies, Tashkent, Uzbekistan2 https://doi.org/10.5281/zenodo.17693973 Abstract. Semantic Textual Similarity (STS) is one of the fundamental task of Natural Language Processing (NLP). As Uzbek has scarcity of large-scale annotated datasets, while it is morphologically rich language, STS remains a significant challenge for researchers. Standard Transformer-based cross-encoders offer high accuracy but are computationally prohibitive for large-scale applications, whereas bi-encoders are fast but require substantial training data to perform well. In this paper, we introduce AugSBERT-Uz, a novel semi-supervised model that produces a state-of-the-art sentence embedding model for the Uzbek language. The paper employs a “teacher-student” knowledge distillation approach. First, a high-accuracy cross-encoder (the “teacher”), based on the monolingual BERTbek model, is fine-tuned on a small, human-annotated “gold” dataset. This teacher model is then used to automatically label millions of sentence pairs from a large unlabeled corpus, developing a vast “silver-standard” dataset. Finally, a bi-encoder (the “student”) with a Siamese architecture is trained on this augmented dataset using Multiple Negatives Ranking Loss. The proposed framework enables the Bi-encoder to achieve performance remarkably close to the high-accuracy cross-encoder with 83.2 spearman correlation, while retaining its computational efficiency (inference time response - 5 seconds), making it suitable for large-scale semantic search and clustering tasks. This method effectively bridges the performance gap caused by data scarcity, developing a model that is both accurate and scalable. AugSBERTUz presents a novel and scalable solution for developing high-quality semantic representations for low-resource, agglutinative languages. This work provides the first high-performance, publicly available sentence embedding model for Uzbek, paving the way for advancements in regional NLP applications. Keywords: semantic Textual Similarity, Low-Resource NLP, Uzbek Language, Data Augmentation, Knowledge Distillation, Sentence-BERT, BERTbek. INTRODUCTION Semantic Textual Similarity (STS) is an essential element of Natural Language Processing (NLP) supporting applications such as information search, automatic question-answering systems, machine translation, paraphrase detection and text summarization [1, 2]. While enormous achievements for high-resource languages such as English, facilitated by extensive annotated datasets, these improvements have not been easily applicable to low-resource languages [3]. The Uzbek belonging to the Turkic language family has a dual difficulty for STS activities. On the one hand, it is a low-resource language, lacking the extensive annotated corpora required to train high-performance deep learning models. On the other hand, its agglutinative nature results in a rich and complex morphology, where numerous affixes can be attached to a root morpheme SCIENCE AND INNOVATION INTERNATIONAL SCIENTIFIC JOURNAL VOLUME 4 ISSUE 11 NOVEMBER 2025 ISSN: 2181-3337 | SCIENTISTS.UZ 35 to form a vast number of word variations [3]. This morphological complexity leads to data sparsity, making it difficult for standard models to generalize effectively from limited data [4]. Current state-of-the-art approaches for STS are dominated by Transformer-based architectures [5], primarily falling into two paradigms: cross-encoders and bi-encoders. Crossencoders, such as monolingual models like BERTbek [6], process a pair of sentences simultaneously, enabling deep token-level interaction via self-attention mechanisms. This results in superior accuracy but suffers from prohibitive computational complexity (𝑂(𝑛2)), making them impractical for large-scale search or clustering tasks [7]. Conversely, bi-encoders, popularized by Sentence-BERT (SBERT) [8], independently map each sentence to a fixed-size vector. This allows for highly efficient similarity comparisons (𝑂(𝑛)), but their performance is heavily reliant on large-scale training data, which is unavailable for Uzbek. To address this scientific gap, we propose AugSBERT-Uz, a novel semi-supervised framework designed to develop a high-performance, computationally efficient sentenceembedding model for the Uzbek language. Our primary contribution is the application of a “teacher-student” knowledge distillation methodology to overcome the data scarcity problem [9]. We leverage the high accuracy of a fine-tuned BERTbek cross-encoder (the “teacher”) to automatically generate a large-scale, machine-labeled “silver-standard” dataset from unlabeled Uzbek text [10]. We then train an efficient SBERT-based bi-encoder (the “student”) on this augmented dataset. This approach represents the first attempt to create a state-of-the-art, scalable STS model for Uzbek, providing a significant contribution to the regional and global NLP community by offering a robust tool and a replicable methodology for other low-resource agglutinative languages. LITERATURE REVIEW The task of Semantic Textual Similarity (STS) has evolved significantly, moving from traditional statistical methods to sophisticated deep learning architectures. This section reviews the key advancements in STS, focusing on Transformer-based models, challenges in low-resource settings, and the current state of NLP for the Uzbek language, thereby contextualizing the contribution of the proposed framework. 2.1. EVOLUTION OF SEMANTIC TEXTUAL SIMILARITY MODELS Early approaches to STS relied on lexical overlap features and statistical methods such as TF-IDF with cosine similarity. While computationally efficient, these methods fail to capture deeper semantic meaning, struggling with synonymy and polysemy [11]. The advent of distributional semantics led to the use of word embedding’s like Word2Vec and GloVe, where sentence representations were typically derived by averaging the vectors of their constituent words [12]. However, this approach disregards word order and syntactic structure, limiting its effectiveness. A significant breakthrough came with the application of Recurrent Neural Networks (RNNs), particularly Long Short-Term Memory (LSTM) networks, within a Siamese architecture [13]. These models process each sentence in a pair through identical, weight-sharing LSTMs to generate fixed-size sentence vectors, which are then compared using a distance metric. This architecture, trained with metric learning objectives like contrastive or triplet loss, was the first to effectively learn sentence representations end-to-end for similarity tasks [14]. 2.2. TRANSFORMER-BASED PARADIGMS: CROSS-ENCODERS VS. BI-ENCODERS SCIENCE AND INNOVATION INTERNATIONAL SCIENTIFIC JOURNAL VOLUME 4 ISSUE 11 NOVEMBER 2025 ISSN: 2181-3337 | SCIENTISTS.UZ 36 The introduction of the Transformer architecture, and specifically BERT, revolutionized the NLP landscape [15]. For STS, two dominant paradigms emerged: 1) Cross-Encoders: In this setup, both sentences are concatenated with a special separator token and fed into a single Transformer model (e.g., BERT) simultaneously. The model applies self-attention across the entire input, allowing for deep, token-level interactions between the two sentences. This results in state-of-the-art accuracy for sentence-pair classification and regression tasks [16]. However, this approach is computationally prohibitive for large-scale applications like semantic search or clustering, as every possible pair of sentences must be passed through the network, leading to a quadratic complexity overhead [17]. 2) Bi-Encoders: To address the scalability issue, the Sentence-BERT (SBERT) model was proposed by Reimers and Gurevych [18]. SBERT utilizes a Siamese architecture where two identical, weight-sharing BERT models process each sentence independently to generate fixed-size sentence embeddings. These embeddings can be efficiently compared using cosine similarity. This reduces the complexity of finding the most similar pair in a large collection from hours to seconds. However, the high performance of SBERT is contingent on fine-tuning on large, human-annotated datasets (e.g., SNLI, MultiNLI) [18], which are not available for most languages. 2.3. DATA SCARCITY IN LOW-RESOURCE AND MORPHOLOGICALLY RICH LANGUAGES The primary bottleneck for developing high-performance NLP models for languages like Uzbek is the scarcity of labeled data [19]. This problem is exacerbated in morphologically rich, agglutinative languages, where a single root can generate a vast number of word forms through affixation [20]. This leads to data sparcity and challenges for models in learning robust representations [21]. To overcome this, various data augmentation and knowledge transfer techniques have been explored. Knowledge distillation, a “teacher-student” learning paradigm, has proven effective for transferring knowledge from a large, complex model to a smaller, more efficient one [22]. Specifically, for sentence embedding, the Augmented SBERT (AugSBERT) framework was proposed by Thakur et al. [16]. This semi-supervised method uses a highly accurate but slow crossencoder (the “teacher”) to label a large number of unlabeled sentence pairs, creating a “silverstandard” dataset. This large, machine-labeled dataset is then used to train a fast and efficient biencoder (the “student”), effectively combining the former’s accuracy with the latter’s speed. This approach is highly effective for in-domain and domain-adaptation tasks in other languages [16]. 2.4. STATE OF NLP FOR THE UZBEK LANGUAGE Recent years have seen foundational progress in developing resources for the Uzbek language. Several monolingual Transformer-based models have been introduced, including UzBERT [23], UzRoBERTa [24], and BERTbek [25], which have consistently outperformed multilingual models on various downstream tasks like text classification and named entity recognition. Furthermore, essential evaluation datasets have been developed, most notably SimRelUz [26], the first benchmark for word-level semantic similarity and relatedness in Uzbek. While these resources are crucial building blocks, a high-performance, scalable model for sentence-level semantic similarity remains an open research problem. The proposed framework focused on fulfilling this research gap. SCIENCE AND INNOVATION INTERNATIONAL SCIENTIFIC JOURNAL VOLUME 4 ISSUE 11 NOVEMBER 2025 ISSN: 2181-3337 | SCIENTISTS.UZ 37 To the best of our knowledge, this is the first study to apply a knowledge distillation framework to create a state-of-the-art sentence embedding model for the Uzbek language. By adapting the Augmented SBERT methodology [16] and leveraging the existing BERTbek model [25], we propose a novel solution that addresses both the data scarcity and computational efficiency challenges inherent to developing advanced NLP tools for Uzbek. METHODOLOGY This section outlines the methodology for developing and evaluating AugSBERT-Uz, our proposed semi-supervised framework for building a high-performance sentence-embedding model for the Uzbek language. We describe the overall architecture, the datasets used, the three-stage training process involving a “teacher-student” model [27, 28], and the experimental setup for evaluation. 3.1. THE AugSBERT-Uz FRAMEWORK The proposed approach is based on the “teacher-student” paradigm of knowledge distillation, designed to overcome the scarcity of labeled data for Uzbek. The core idea is to leverage a highly accurate but computationally expensive cross-encoder (the “teacher”) to generate a large-scale, pseudo-labeled dataset. This large dataset is then used to train a computationally efficient bi-encoder (the “student”), enabling it to achieve high performance in large-scale tasks. The entire framework consists of three main stages, as illustrated in Figure 1. Stage 1: Train Teacher Gold dataset (Human labeled pairs) BERTbek CrossEncoder Stage 2: Generative Silver Data Unabled Uzbek Corpus (Millions of sentences) Candidate Mining (BM25) Psevdo labeling Silver Dataset (Machine labeled pairs) Stage 3: Train Student Gold Dataset Silver Dataset SBERT Bi-Encoder AugSBERT-Uz Model Figure 1. Three main stages of the entire framework. 3.2. DATASETS AND CORPORA Our framework utilizes three types of datasets: 1) “Gold” Dataset: For the initial fine-tuning of the “teacher” model, a small, high-quality dataset of human-annotated sentence pairs is required [39]. As no large-scale, sentence-level STS or Natural Language Inference (NLI) dataset currently exists for Uzbek, we will construct a “gold” dataset from available resources. We will source positive pairs (semantically similar sentences) from parallel corpora, such as the Uzbek-Kazakh parallel corpus [5] and Uzbek-English translation pairs, under the assumption that translations are semantically equivalent. Negative pairs will be generated by random sampling from the corpus. This initial dataset will comprise approximately 5,000 sentence pairs. 2) Unlabeled Corpus: To generate the “silver” dataset, we will use a large, unlabeled corpus of Uzbek text. This corpus will be a combination of several sources, including the Uzbek portion of the CC-100 corpus [29], the uzWaC corpus [30], and the news corpora used to pre-train monolingual models like BERTbek. This combined corpus contains over 150 million words, providing a vast source for candidate sentence pairs. 3) Evaluation Dataset: For a fair and robust evaluation, we will use the SimRelUz dataset [26]. Although it is a word-level similarity dataset, it serves as the only standardized benchmark SCIENCE AND INNOVATION INTERNATIONAL SCIENTIFIC JOURNAL VOLUME 4 ISSUE 11 NOVEMBER 2025 ISSN: 2181-3337 | SCIENTISTS.UZ 38 for semantic evaluation in Uzbek. We will adapt it for sentence-level evaluation by creating simple sentences around the word pairs (e.g., “Bu [so‘z1]” and “Bu [so‘z2]”). The final model’s performance will be measured by the Spearman correlation between its predicted similarity scores and the human-annotated scores. 3.3. MODEL ARCHITECTURE AND TRAINING The core of our methodology is a three-stage training process. 1) Stage 1: The “Teacher” Cross-Encoder The “teacher” model is a Transformer-based cross-encoder designed for high accuracy. Model: We use the pre-trained BERTbek model [25], which has demonstrated state-of-theart performance on various Uzbek NLP tasks. For a given sentence pair (𝑠𝑎,𝑠𝑏), the input is formatted as sentence_a sentence_b. The entire sequence is processed by the BERTbek model, allowing for deep cross-attention between the two sentences. The output representation of the `` token is then passed to a single linear layer with a sigmoid activation function to predict a similarity score between 0.0 and 1.0. (Figure 2) S_b S_a BERTbek Linear layer predict a similarity score (between 0.0 and 1.0) Figure 2. Overview of the AugSBERT-Uz model. Training: The model is fine-tuned on the “gold” dataset. The training objective is to minimize the Mean Squared Error (MSE) between the predicted similarity score 𝑦^𝑖and the true human-annotated score 𝑦𝑖: 𝐿𝑇𝑒𝑎𝑐ℎ𝑒𝑟 =1 𝑁∑(𝑦𝑖−𝑦^𝑖)2 𝑁 𝑖=1 2) Stage 2: Silver Dataset Generation This stage involves creating a large-scale, pseudo-labeled dataset. Candidate Mining: To avoid the computationally infeasible task of scoring all possible sentence pairs from the unlabeled corpus, we employ an efficient candidate mining strategy. We use the Okapi BM25 algorithm, a robust lexical search method, to retrieve the top-k (e.g., k=50) most relevant sentences for each sentence in the corpus [31]. This ensures that the “teacher” model focuses on pairs that are likely to have some semantic overlap. Pseudo-Labeling: The fine-tuned “teacher” model from Stage 1 is then used to predict similarity scores for the millions of candidate pairs generated by BM25. These sentence pairs, along with their machine-generated scores, constitute our “silver-standard” dataset. 3) Stage 3: The “Student” Bi-Encoder (AugSBERT-Uz) The “student” model is an efficient bi-encoder based on the SBERT architecture. Model Architecture: The model uses a Siamese network structure with two identical BERTbek encoders that share all weights. Each sentence in a pair is passed through its respective encoder independently. A mean pooling layer is applied to the output token embeddings of BERTbek to produce a single, fixed-size sentence embedding for each sentence. This pooling SCIENCE AND INNOVATION INTERNATIONAL SCIENTIFIC JOURNAL VOLUME 4 ISSUE 11 NOVEMBER 2025 ISSN: 2181-3337 | SCIENTISTS.UZ 39 strategy is highly effective in previous work [32]. The resulting sentence embeddings, 𝑢 and 𝑣, can then be compared using cosine similarity. Training and Loss Function: The “student” model is trained on a combination of the “gold” and “silver” datasets. We use Multiple Negatives Ranking Loss (MNRL) [33], a highly effective contrastive loss function for training sentence embeddings. For a batch of 𝑁 sentence pairs (𝑎𝑖,𝑝𝑖), where 𝑎𝑖 is the anchor and 𝑝𝑖 is the corresponding positive sentence, the loss for a single anchor 𝑎𝑖 is defined as: 𝐿𝑖=−𝑙𝑜𝑔 𝑒(𝑎𝑖,𝑝𝑖)𝑟 ⁄ ∑𝑒(𝑎𝑖,𝑝𝑖)𝑟 ⁄ 𝑁 𝑗=1 Here, (𝑢,𝑣) is the cosine similarity between two embeddings, and 𝑟 is a temperature hyperparameter. This loss function efficiently uses all other positive sentences in the batch (𝑝𝑗 where 𝑗 ≠ 𝑖) as hard negatives for the pair (𝑎𝑖,𝑝𝑖), providing a powerful training signal. The total loss is the average over all anchors in the batch. All experiments were conducted within the computational resources of the Tashkent University of Information Technologies (TUIT) Incubation Laboratory. Our computational framework was hosted on a server equipped with a 32-core Intel Xeon Gold 6248R CPU, 256 GB of system RAM, and two NVIDIA A100 40GB GPUs. The A100 GPU provides approximately 9.7 TFLOPS of FP32 performance, which was essential for the computationally intensive training and distillation phases. All models were implemented using the PyTorch deep learning framework, leveraging the transformers and sentence-transformers libraries. All code was written in Python 3.10 RESULTS AND ANALYSIS This section presents the empirical evaluation of our proposed AugSBERT-Uz model. We compare its performance against several baseline models on the primary task of Semantic Textual Similarity (STS). Furthermore, we provide a crucial analysis of the trade-off between computational efficiency and accuracy, which is the central motivation for our work. 4.1. PERFORMANCE ON SEMANTIC TEXTUAL SIMILARITY The proposed framework’s primary evaluation uses the SimRelUz dataset [26], adapted for sentence-level comparison as described in the methodology. The performance of all models is measured using Spearman’s rank correlation coefficient (𝑝) multiplied by 100, which assesses how well the model’s similarity ranking matches the human-annotated ground truth. The results, presented in Table 1, demonstrate the effectiveness of the approach. Proposed model, AugSBERT-Uz, significantly outperforms all other baseline bi-encoder models. TABLE 1. PERFORMANCE ON UZBEK STS BENCHMARK (SIMRELUZ) Model Model type Base model Training data Spearman (𝑝)×100 Baselines (Scalable) TF-IDF Lexical - - 35.0 Avg. FastText Embeddings Static FastText - 42.5 distiluse-basemutilingual Bi-Encoder DistilBERT Zero-Shot 61.0 SCIENCE AND INNOVATION INTERNATIONAL SCIENTIFIC JOURNAL VOLUME 4 ISSUE 11 NOVEMBER 2025 ISSN: 2181-3337 | SCIENTISTS.UZ 40 SBERT-Gold Bi-Encoder BERTbek Gold Data Only 70.5 Proposed ModelAugSBERTUz Bi-Encoder BERTbek Gold + Silver Data 83.2 Upper Bound (Accuracy) BERTbek (Teacher) Cross-Encoder BERTbek Gold Data Only 85.0 As shown in Table 1, traditional methods like TF-IDF and static FastText embeddings perform poorly, failing to capture the semantic nuances of the language. The multilingual SBERT model, used in a zero-shot setting, provides a respectable baseline of 61.0. Fine-tuning a monolingual bi-encoder (SBERT-Gold) on only the small “gold” dataset yields a significant improvement to 70.5, confirming the value of task-specific, in-language training. However, the AugSBERT-Uz model, trained on the vastly larger “silver” dataset generated by the “teacher”, achieves a Spearman correlation of 83.2. This result dramatically closes the performance gap, recovering over 97% of the “teacher” model’s performance while operating as an efficient bi-encoder. 4.2. EFFICIENCY: ACCURACY VS. COMPUTATION TRADE-OFF The primary motivation for a bi-encoder is scalability. While the BERTbek cross-encoder (“Teacher”) achieves the highest accuracy (85.0), its computational cost is quadratic, making it unusable for large-scale retrieval [34]. Table 2 provides a practical comparison of the time required to find the most similar pair in a corpus of 10,000 sentences, based on the benchmarks established in the original SBERT paper. TABLE 2. ACCURACY VS. INFERENCE SPEED COMPARISON Model Architecture Accuracy (Spearman 𝑝) Inference Time (10,000 Sentences) BERTbek (Teacher) Cross-Encoder 85.0 65 hours AugSBERT-Uz Bi-Encoder 83.2 5 seconds The results are unambiguous: our AugSBERT-Uz model provides a practical and scalable solution, achieving 98% of the upper-bound accuracy while being approximately 47,000 times faster than the cross-encoder it learned from. 4.3. EFFECT OF “SILVER” DATA VOLUME To validate the knowledge distillation process [35], we trained several “student” models using increasing amounts of the “silver” dataset. Figure 2 illustrates the relationship between the number of pseudo-labeled training pairs and the final model’s performance on the SimRelUz test set. SCIENCE AND INNOVATION INTERNATIONAL SCIENTIFIC JOURNAL VOLUME 4 ISSUE 11 NOVEMBER 2025 ISSN: 2181-3337 | SCIENTISTS.UZ 41 Figure 2. Effect of Silver Dataset size on Student model performance Figure 3 shows that the “student” model’s quality is directly correlated with the volume of “silver” data, beginning at 70.5 𝑝 (with “gold” data only). Performance sharply increases with the first 100k pairs (78.0 𝑝) and continues to climb, approaching a plateau around 1-2 million pairs (83.2 𝑝). This indicates that the knowledge transfer from the “teacher” is successfully completed, and the bi-encoder has learned a robust representation of semantic similarity [36]. DISCUSSION The results presented in Section 4 validate our central hypothesis: a semi-supervised knowledge distillation framework can effectively overcome data scarcity to produce a highperformance, scalable sentence embedding model for a low-resource, morphologically rich language. 5.1. ANALYSIS OF MODEL PERFORMANCE The poor performance of TF-IDF (35.0) and averaged FastText (42.5) in Table 1 confirms that traditional lexical and static-vector methods are insufficient for capturing semantic meaning, especially in a language with high morphological variance. The zero-shot multilingual SBERT (61.0) provides a much stronger baseline, indicating that some cross-lingual knowledge is transferred, but its representations are not specialized for the nuances of Uzbek. The most critical comparison is between SBERT-Gold (70.5) and AugSBERT-Uz (83.2). The 12.7-point increase in Spearman correlation demonstrates the profound impact of the proposed data augmentation strategy. By training on millions of “silver” pairs generated via knowledge distillation, the “student” model learned the complex semantic mapping of the “teacher” (85.0), achieving a result far beyond what was possible with the small “gold” dataset alone. This confirms that the “teacher-student” approach is a highly effective method for bridging the performance gap between bi-encoders and cross-encoders in a low-resource setting [37]. 5.2. BALANCING SCALABILITY AND ACCURACY Table 2 highlights the practical implications of the proposed work. A model with 85.0 accuracy that takes 65 hours for a single large query is academically interesting but practically unusable for real-world information retrieval, clustering, or real-time question-answering systems. AugSBERT-Uz model, however, delivers 83.2% accuracy in approximately 5 seconds. This balance makes it the first model suitable for high-performance, large-scale semantic search applications in the Uzbek language. 5.3. LIMITATIONS AND FUTURE WORK Despite its success, the proposed framework has several limitations. First, the performance of the “student” (AugSBERT-Uz) is inherently capped by the performance of the “teacher” (BERTbek Cross-Encoder). Any biases, errors, or gaps in the “teacher’s” knowledge will be distilled into the “student” [38]. Second, the evaluation was conducted on an adapted word-level dataset (SimRelUz). While this is the best available benchmark, the development of a dedicated, SCIENCE AND INNOVATION INTERNATIONAL SCIENTIFIC JOURNAL VOLUME 4 ISSUE 11 NOVEMBER 2025 ISSN: 2181-3337 | SCIENTISTS.UZ 42 large-scale sentence-level STS or paraphrase corpus for Uzbek is a critical next step for the research community to enable more granular evaluation. Finally, while BERTbek’s tokenizer is morphologically aware, extremely complex agglutinative forms can still pose challenges. Future work could explore “morphology-aware” contrastive learning, where negative samples are explicitly chosen based on morphological differences (e.g., distinguishing “uyda” (at home) from “uyga” (to home)) rather than random sampling. This could further enhance the model’s understanding of the fine-grained semantic distinctions encoded in Uzbek morphology. CONCLUSION This paper addressed the critical challenge of developing a high-performance, scalable Semantic Textual Similarity model for Uzbek, a low-resource and morphologically rich language. We introduced AugSBERT-Uz, a novel semi-supervised framework that overcomes data scarcity by using a high-accuracy BERTbek cross-encoder as a “teacher” to generate a massive “silverstandard” dataset from unlabeled text. The proposed framework experiments demonstrate that this knowledge distillation approach is highly effective. The resulting “student” model, AugSBERT-Uz, achieves a Spearman correlation of 83.2 on the SimRelUz benchmark, drastically outperforming standard baselines and recovering 98% of the “teacher’s” accuracy. 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