International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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A Comparative Analysis of BLEU and BERT Score for Evaluating Machine Translation of a Telugu Poem

Author(s) Prof. Madhu Jyoti Kolakaluri, Dr. Revathi Kudumula, Prasanna Guduru
Country India
Abstract The proliferation of Machine Translation (MT) systems has necessitated robust automatic evaluation metrics. While BLEU (Bi-Lingual Evaluation Understudy) and BERT
(Bidirectional Encoder Representations from Transformers) These are the longstanding standards for assessing lexical and n-gram overlap. Its efficacy in evaluating semantically complex and culturally nuanced texts, such as poetry and prose, remains limited. This study presents a comparative evaluation of five NMT systems—Google Translate, Microsoft Translator, Quill Bot, Gork, and Bhashini on a corpus of Telugu literary texts, the poem "Bandipotlu". This research employs both the traditional BLEU metric and the context-aware BERT metric scores to analyse machine translation quality. Our findings indicate a significant divergence in the rankings provided by these metrics. BLEU scores are heavily penalizing creative paraphrasing and stylistic variations, whereas BERTScores leverage semantic embeddings, and they demonstrate a higher correlation with human intuitions for literary translation. The results suggest that BERTScore is a more suitable metric for evaluating the preservation of meaning, tone, and cultural nuance in literary machine translation, and it advocates for a paradigm shift beyond surface-level n-gram matching.
Keywords Machine Translation, Evaluation Metrics, BLEU, BERTScore, Telugu Literature, Literary Translation, Natural Language Processing
Field Arts
Published In Volume 8, Issue 1, January-February 2026
Published On 2026-01-31
DOI https://doi.org/10.36948/ijfmr.2026.v08i01.67737
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