Simplifying Translations for Children: Iterative Simplification Considering Age of Acquisition with LLMs
This addresses the issue of children struggling to understand translations due to complex vocabulary, though it is incremental as it builds on existing NMT and LLM techniques.
The paper tackles the problem of neural machine translation producing complex words for children by proposing a method to replace high Age of Acquisition words with simpler ones using large language models, achieving effective word replacement while maintaining high BLEU and COMET scores.
In recent years, neural machine translation (NMT) has been widely used in everyday life. However, the current NMT lacks a mechanism to adjust the difficulty level of translations to match the user's language level. Additionally, due to the bias in the training data for NMT, translations of simple source sentences are often produced with complex words. In particular, this could pose a problem for children, who may not be able to understand the meaning of the translations correctly. In this study, we propose a method that replaces words with high Age of Acquisitions (AoA) in translations with simpler words to match the translations to the user's level. We achieve this by using large language models (LLMs), providing a triple of a source sentence, a translation, and a target word to be replaced. We create a benchmark dataset using back-translation on Simple English Wikipedia. The experimental results obtained from the dataset show that our method effectively replaces high-AoA words with lower-AoA words and, moreover, can iteratively replace most of the high-AoA words while still maintaining high BLEU and COMET scores.