Enhancing Masked Word Prediction in T amil Language Models: A Synergistic Approach Using BERT and SBERT (2024)

Abstract: This research work presents a novel approach to enhancing masked word prediction and sentence-level semantic analysis in Tamil language models. By synergistically combining BERT and Sentence-BERT (SBERT) models, we leverage the strengths of both architectures to capture the contextual understanding and semantic relationships in Tamil Language sentences. Our methodology incorporates sentence tokenization as a crucial pre-processing step, preserving the grammatical structure and word-level dependencies of Tamil sentences. We trained BERT and SBERT on a diverse corpus of Tamil data, including synthetic datasets, the Oscar Corpus, AI4Bharat Parallel Corpus, and data extracted from Tamil Wikipedia and news websites. The combined model effectively predicts masked words while maintaining semantic coherence in generated sentences. While traditional accuracy metrics may not fully capture the model’s performance, intrinsic and extrinsic evaluations reveal the model’s ability to generate contextually relevant and linguistically sound outputs. Our research highlights the importance of sentence tokenization and the synergistic combination of BERT and SBERT for improving masked word prediction in Tamil sentences.

## introduction in the era of digital communication, the significance of natural language processing (nlp) extends beyond the mere processing of common languages like english or mandarin to encompass regional and less commercially prominent languages. tamil is a language spoken by millions in india and the global tamil diaspora, presents unique challenges and opportunities in the realm of nlp. unlike indo-european languages that have been the focus of most nlp research, tamil language have features which are highly agglutinative structure, rich morphological forms and syntactic complexity that differ markedly from those languages. these features require specialized approaches for effective processing and understanding in automated systems. traditional nlp models like bert (bidirectional encoder representations from transformers) have revolutionized the way machines understand human languages by learning context dependent word meanings from vast amounts of text. however, when applied to languages like tamil, these models often struggle due to the lack of sufficiently large and diverse training datasets, as well as the intrinsic linguistic features. in addition, while bert models may perform well at capturing contextual information at the word level, they may not work as effectively with sentence-level semantics, which are particularly important for languages with complex sentence structures, such as tamil. in order to address and to overcome these difficulties, our research work combines bert with sentence-bert (sbert), a variant of the standard bert model that focus to represent sentences in a semantic space for better understanding of the sentence meaning. this approach aims to make use of the strengths of both bert, with its deep wordlevel context understanding, and sbert model with its ability to produce meaningful sentence embeddings. by training these models with a detailed data set, this research work explained how these integrated approach can increase both the accuracy and the semantic coherence of language models for tamil language . ## methodology our research work have aim to enhance the performance of masked word prediction and sentencelevel semantic analysis for the tamil sentences using an integrated bert and sentence-bert (sbert) framework. this section outlines the methodologies employed in data collection, preprocessing, model training and evaluation. the figure 1 represents the proposed model for the mask word prediction for tamil sentences. our methodology is underpinned by detailed preprocessing steps, including sentence tokenization and vectorization, tailored to maintain the linguistic integrity of tamil sentences. the datasets have used in this research work comprising synthetic data, the oscar corpus, ai4bharat parallel corpus and extracts from tamil wikipedia and news website were selected to provide a broad spectrum of language uses from formal to colloquial contexts. the performance of our combined model have evaluated using both traditional metrics such as precision, recall, and f1 score as well as advanced metrics like bleu and rouge scores which estimate the coherence and contextual relevance of the generated content (apallius de @xcite . 3 data collection and data set this paper works have aim on general-purpose datasets such as the oscar corpus and synthetic datasets. these datasets have contain a broad range of formal and informal tamil text but do not specifically include domain-specific content like legal or medical texts. figure 2 , represents the dataset details. ## conclusion by fine-tuning models on low-resource tamil datasets, we have demonstrated that even with limited data, substantial progress can be made in improving the understanding of tamil syntax and semantics. our model showcases strong performance in balancing precision (0.780) and recall (0.820), leading to a f1 score of 0.800. these results affirm the model's ability to make relevant and accurate predictions while maintaining a good balance between identifying true positives and minimizing false positives and negatives @xcite . another strength is the diversity score of 0.95, reflecting the model's ability to generate varied and creative outputs, which is highly valuable for applications like text generation. this is complemented by a coherence score of 0.750, suggesting that the model maintains logical flow in its generated outputs. however, there is still to improve coherence further to ensure greater consistency @xcite . the perplexity value of 150.000 indicates that the model occasionally struggles with uncertainty in predicting the correct word for the mask token. however, even when the model did not predict the exact expected word, it often selected a word that made the sentence meaningful @xcite . this observation led us to explore combining the bert model with an sbert model to semantically verify the generated sentences, ensuring that the predicted words fit naturally within the sentence context, even when lexical accuracy was low. this hybrid approach enhances the model's capability to generate more contextually appropriate outputs. the model showcases strong performance in balancing precision (0.780) and recall (0.820), resulting in an f1 score of 0.800. by conducting language-specific adaptations, the approach can be extended to enhance nlp capabilities for other dravidian languages, addressing tasks such as masked word prediction and sentence-level semantic analysis. in conclusion, this research establishes a strong foundation for tamil nlp and highlights the potential for broader applications across dravidian languages. while the current results are promising, further exploration of ensemble learning, domainspecific fine-tuning, and scalability will pave the way for more advanced and reliable language models in the future.