# Simplifying Translations for Children: Iterative Simplification Considering Age of Acquisition with LLMs

arXiv: https://arxiv.org/abs/2408.04217 · HTML: https://mlsanity.com/paper/2408.04217/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 26 references that are in the map.
- First posted on arXiv 8 Aug 2024.

## Builds on (most cited first, 8 of 8)

- [COMET: A Neural Framework for MT Evaluation](https://mlsanity.com/paper/2009.09025/) (2020)
- [Improving Neural Machine Translation Models with Monolingual Data](https://mlsanity.com/paper/1511.06709/) (2015)
- [Sentence Simplification with Deep Reinforcement Learning](https://mlsanity.com/paper/1703.10931/) (2017)
- [Controlling Text Complexity in Neural Machine Translation](https://mlsanity.com/paper/1911.00835/) (2019)
- [Dynamic Multi-Level Multi-Task Learning for Sentence Simplification](https://mlsanity.com/paper/1806.07304/) (2018)
- [Joint Training for Neural Machine Translation Models with Monolingual Data](https://mlsanity.com/paper/1803.00353/) (2018)
- [JParaCrawl v3.0: A Large-scale English-Japanese Parallel Corpus](https://mlsanity.com/paper/2202.12607/) (2022)
- [Controlling Pre-trained Language Models for Grade-Specific Text Simplification](https://mlsanity.com/paper/2305.14993/) (2023)

## Built on by (most cited first, 5 of 5)

- [An Interdisciplinary Approach to Human-Centered Machine Translation](https://mlsanity.com/paper/2506.13468/) (2025)
- [Difficult for Whom? A Study of Japanese Lexical Complexity](https://mlsanity.com/paper/2410.18567/) (2024)
- [Towards Automated Lexicography: Generating and Evaluating Definitions for Learner's Dictionaries](https://mlsanity.com/paper/2601.01842/) (2026)
- [Let's Simplify Step by Step: Guiding LLM Towards Multilingual Unsupervised Proficiency-Controlled Sentence Simplification](https://mlsanity.com/paper/2602.07499/) (2026)
- [Toward Machine Interpreting: Lessons from Human Interpreting Studies](https://mlsanity.com/paper/2508.07964/) (2025)

## Abstract (arXiv)

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.