Entity-aware Multi-task Training Helps Rare Word Machine Translation
Matīss Rikters, Makoto Miwa · 2024
Named entities (NE) are integral for preserving context and conveying accurate information in the machine translation (MT) task.Challenges often lie in handling NE diversity, ambiguity, rarity, and ensuring alignment and consistency.In this paper, we explore the effect of NE-aware model fine-tuning to improve the handling of NEs in MT.We generate data for NE recognition (NER) and NE-aware MT using common NER tools from Spacy and align entities in parallel data.Experiments with finetuning variations of pre-trained T5 models on NE-related generation tasks between English and German show promising results with increasing amounts of NEs in the output and BLEU score improvements compared to the non-tuned baselines.