This model follows Approach 2 (Direct Fine-tuning) described in the research. It is the Combined Finetuned (1x Punct) variant, where the base model was fine-tuned on the IITB-ENG-MAR dataset using a strategy that alternates between keeping and removing punctuation in the source English text. This helps the model implicitly learn context and resolve semantic ambiguities (e.g., distinguishing "Honey" as a name versus a substance) even when punctuation is missing.
The model is intended for English-to-Marathi translation tasks, particularly those involving informal or unpunctuated source text (like social media or speech transcripts) where standard models might struggle with meaning-changing ambiguities.
Training and Evaluation Data
Fine-tuning Data: A variant of the IITB-ENG-MAR dataset.
Evaluation Benchmark:Virām (Punct-Eng-Mar/PEM), a diagnostic benchmark of 54 manually curated, punctuation-ambiguous instances designed to test MT robustness.
Training Results
The model achieves the following results on the evaluation set:
Loss: 0.4160
Bleu: 9.5026
Chrfpp: 31.6551
Comet: 0.5336
Gen Len: 20.8752
Training Procedure
Training Hyperparameters
The following hyperparameters were used during training:
