Machine translation errors as learning resources: Developing an English-Indonesian dataset for English language teaching - Firqo Amelia; Aenor Rofek; Evynurul Laily Zen (Universitas Syiah Kuala, 2026)
This study aims to develop an Indonesian Grammatical Error Correction (GEC) dataset using Statistical Machine Translation (SMT) and Sederet.com as the primary source of grammatical errors. Additionally, this research seeks to identify the types of errors in Indonesian translations of English sentences produced by Machine Translation (MT) that can serve as learning resources in English language teaching. This study extracts data from social media (X) as texts and processes them across three main stages to construct a GEC dataset. The first stage involves data collection, comprising the source, target, and control texts. The second stage consists of translation error analysis, which is conducted using Nord’s (2005) Translation Problems Theory. The third stage involves data annotation, which is performed using the UAM CorpusTool software. To identify translation errors, this study compared MT translations with those of professional translators, which were then validated by professional editors. The findings revealed that linguistic errors, particularly those related to semantics, diction, synonymy, conjunctions, and prepositions, were the most prevalent and relevant categories for inclusion in the dataset. These errors were identified through comparisons between the target and control texts and were subsequently annotated. Through the stages of schema creation, data input, error labelling, and correction insertion, a fully annotated corpus was produced. The implications of this study extend to both research and pedagogy. The dataset model supports the advancement of Indonesian GEC systems and offers teachers authentic materials to engage learners in translation-based activities.