Towards Readability-Controlled Machine Translation of COVID -19 Texts (2022)

Abstract: This project investigates the capabilities of Machine Translation models for generating translations at varying levels of readability, focusing on texts related to COVID-19. Whilst it is possible to automatically translate this information, the resulting text may contain specialised terminology, or may be written in a style that is difficult for lay readers to understand. So far, we have collected a new dataset with manual simplifications for English and Spanish sentences in the TICO-19 dataset, as well as implemented baseline pipelines combining Machine Translation and Text Simplification models.

## introduction the project has the following objectives: (1) to collect a dataset with simplified versions of parallel texts in english and spanish about covid-19; the first two goals of the project were carried out from january 2021 to december 2021, and resulted in the release of the simple tico-19 dataset @xcite ). 1 we continue to work with the new dataset to further investigate the nature of readability-controlled output generation in the mt context. we hope to apply for further funding at a national and european level as a result of this work. ## the simple tico-19 dataset we leveraged the tico-19 benchmark (anastasopoulos et al., 2020), which contains 3,000 sentences related to the covid-19 pandemic, translated from english into 36 languages and from several sources (e.g. academic publications, speech corpora, news articles, etc.). for our project, we collected manual simplifications for the english and spanish subsets, resulting in the simple tico-19 dataset, where each sentence has either a simplified version of itself, or a decision has been taken that the sentence is already sufficiently simple. ## machine translation baselines to obtain baseline results, we leveraged models pre-trained on opus-mt-en-es with marianmt as architecture. controllable translation. we will study how to train models that generate outputs at diverse readability levels. we will explore varying the proportion of translation and simplification training instances to control the readability of the translations. we will rank target-side simple sentences according to the proportion of complex words and syntactic complexity, and use this ranked list to create different readability levels that allow training models for multiple degrees of complexity. evaluation. we will develop novel metrics suitable for the joint translation and simplification task, specifically for the medical domain. for instance, we will combine traditional similaritybased metrics, such as bleu and bertscore, with readability indices. while the latter are more suitable for analysing documents, we plan to adapt them for sentence-level assessment using complex word identification approaches and heuristics. we will then measure the correlation of our new metrics with human judgements on adequacy and simplicity of automatic translations.