What do post-editors correct? A fine-grained analysis of SMT and NMT errors
The recent improvements in neural MT (NMT) have driven a shift from statistical MT (SMT) to NMT. However, to assess the usefulness of MT models for post-editing (PE) and have a detailed insight of the output they produce, we need to analyse the most frequent errors and how they affect the task. We p...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2021 |
| País: | España |
| Institución: | Universitat Oberta de Catalunya (UOC) |
| Repositorio: | O2, repositorio institucional de la UOC |
| OAI Identifier: | oai:openaccess.uoc.edu:10609/150203 |
| Acceso en línea: | http://hdl.handle.net/10609/150203 https://doi.org/10.5565/rev/tradumatica.286 |
| Access Level: | acceso abierto |
| Palabra clave: | machine translation MT NMT post-editing neuram machine translation error taxonomy traducció automàtica taxonomia d'errors traducció automàtica neuronal postedició TAN TA traducción automática taxonomía de errores posedición traducción automática neuronal |
| Sumario: | The recent improvements in neural MT (NMT) have driven a shift from statistical MT (SMT) to NMT. However, to assess the usefulness of MT models for post-editing (PE) and have a detailed insight of the output they produce, we need to analyse the most frequent errors and how they affect the task. We present a pilot study of a fine-grained analysis of MT errors based on post-editors corrections for an English to Spanish medical text translated with SMT and NMT. We use the MQM taxonomy to compare the two MT models and have a categorized classification of the errors produced. Even though results show a great variation among posteditors’ corrections, for this language combination fewer errors are corrected by post-editors in the NMT output. NMT also produces fewer accuracy errors and errors that are less critical. |
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