Simplifying Translations for Children: Iterative Simplification Considering Age of Acquisition with LLM s (2024)

Abstract: In recent years, neural machine translation (NMT) has become 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 high Age of Acquisitions (AoA) words 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.

## introduction neural machine translation (nmt) has seen significant progress in recent years, making it practical for everyday use. as a result, more and more people are using it to meet their translation needs. nmt systems can generate fluent and grammatically correct sentences in most cases. however, some people may have difficulty understanding the translations due to the complexity of the words used. this is an especially serious risk when children use nmt systems. for example, when using an nmt system to translate textbooks used in the primary schools of another country, the system may use words that are not appropriate for primary we have released our code and dataset at https:// github.com/nttcslab-nlp/simplifyingmt_acl24 school students in the country of the target language. this is because nmt systems do not take into account the complexity of the words used to compose the translations. additionally, the parallel corpus used for the training, which mostly comes from the web, is biased because words used on the web are generally more complex than those used for children. therefore, a mechanism is needed to control the complexity of words in nmt systems. one way to measure such complexity is through the age of acquisition (aoa), which is the average age at which a person learns a particular word. words with higher aoa are more difficult to learn. by controlling the aoa of words used in translations, we can simplify the translated sentence to a difficulty level that is more appropriate to the user. however, merely replacing words based on their aoa may not sufficiently simplify the sentence. another approach is using paraphrase models trained with a parallel corpus of complex and simple sentences, but this method cannot control the aoa of words. this paper proposes a method for appropriately simplifying translations to particular user levels, such as children, through post-editing. figure 1 shows an overview of our method. we replace a high-aoa word in a translation with a simple one by using a large language model (llm) while giving the source sentence. this technique allows us to replace not only the target word but also the surrounding words within the context of the sentence, which helps to simplify the overall sentence while preserving its original meaning. moreover, we can apply this process iteratively to simplify all high-aoa words in a translation or in a partially simplified sentence that still contains complex words. furthermore, users are able to customize the editing process on their own by specifying particular words that need to be replaced. this feature allows a more personalized editing experience. the plebiscite was held. ## proposed method post-editing of initial translations is a promising approach to providing child-appropriate translations, given the difficulty of controlling the aoa of words in mt systems. one approach to achieve this is by simply replacing high aoa words in translations with lower aoa ones using a thesaurus. however, word-to-word replacement may sometimes change the meaning of the original sentences due to mismatched collocations. on the other hand, existing text simplification techniques are also available, but they do not guarantee that simplified translations consist of only appropriate aoa words. to address these issues, we propose a method for iteratively applying llms to explicitly replace a high-aoa word with a lower one. llms are advanced language models that can reconstruct phrases containing high-aoa words, meaning they can significantly alter translations without changing their meanings. we fine-tuned an llm to generate a simplified sentence from a given initial translation, the source sentence foot_2 , and a word whose aoa is greater than the specific value in the translation (figure 1 ). we assumed that using the source sentences to simplify the translations would maintain the original meaning despite replacing the high-aoa words. high-aoa words in translations are enclosed @xmath0. since our method determines the words to be edited based on their aoa, it allows for easy tailoring of the mt system based on the user's age. in other words, when the mt system users are n-yearold children, our system generates translations by replacing words with an aoa greater than n with words with an aoa less than or equal to n. in addition, since users themselves can specify words, it is possible to simplify a sentence by specifying words that a particular user does not understand, regardless of their age. this method can be used iteratively, and thus if a translation contains multiple words that need to be replaced, all of these words can be replaced by repeatedly specifying words. in addition, if the aoa does not decrease after one round of simplification, it is possible to iteratively continue simplification until the aoa is below the criterion by again specifying the target words. the prompts used in the experiments are shown in ## conclusion we proposed a method to provide easy-tounderstand translations for children. our method involves iteratively replacing high-aoa words in a translation with lower-aoa ones using llms. this is done by providing a triple of the source sentence, the translation, and the target word to be replaced. we also automatically generated a dataset from a monolingual corpus to evaluate simplification based on the back-translation technique. moreover, we compared our method with baseline methods, namely, muss, constraint generation, automatic post-editing, and direct translation, and we tested them on ten-year-old children (@xmath1. the re- sentence highest aoa word source この用語は、アルバム上の特定の曲を数字で表すためによく使用されます。 initial translation this term is often used to denote certain songs on the album by numbers. denote (11.24) muss this term is often used to show how many songs are on the album. term (8.28) constraint generation the term is often used to describe a specific song on an album in numbers. specific (9.28) ape the term is often used to describe certain songs on the album by numbers. term (8.28) direct translation this word is often used to represent a specific song on an album with numbers. represent (10.33) multi-word the term is often used to describe certain songs on an album by numbers. term (8.28) iteration 1 the term is often used to represent a particular song on a given album by numbers. represent (10.33) iteration 2 the term is often used to describe certain songs on a given album by numbers. term (8.28) reference the term is often used to mean a specific song on the album by number. specific (9.28) 75) iteration 1 but its origin was first explored by foreigners in 1951 after 453 years. foreigners (10.39) iteration 2 however, its origins were first investigated by foreign people in 1951 after 453 years. origins (10.25) iteration 3 however, its origins were first explored in 1951 by foreigners 453 years later. foreigners (10.39) iteration 4 but its origins were first examined in 1951 by foreign people 453 years later. origins (10.25) iteration 5 its origins, however, were first looked at in 1951 by foreign researchers, after 453 years. origins (10.25) reference its source, however, was first explored by non-native people in 1951, 453 years later. native (9.20)