EnerGIZAr: Leveraging GIZA++ for Effective Tokenizer Initialization

Continual pre-training has long been considered the default strategy for adapting models to non-English languages, but struggles with initializing new embeddings, particularly for non-Latin scripts.In this work, we propose En-erGIZAr, a novel methodology that improves continual pre-training by leveraging statistical word alignment techniques.Our approach utilizes GIZA++ to construct a subword-level alignment matrix between source (English) and target language tokens.This matrix enables informed initialization of target tokenizer embeddings, which provides a more effective starting point for adaptation.We evaluate EnerGIZAr against state-of-the-art initialization strategies such as OFA and FOCUS across four typologically diverse languages: Hindi, Basque, Arabic and Korean.Experimental results on key NLP tasks -including POS tagging, Sentiment Analysis, NLI, and NER -demonstrate that EnerGIZAr achieves superior monolingual performance while also out-performing all methods for cross-lingual transfer when tested on XNLI.With EnerGIZAr 1 , we propose an intuitive, explainable as well as state-of-the-art initialisation technique for continual pre-training of English models.