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Applying Incremental Learning to Post-editing Systems: Towards Online Adaptation for Automatic Post-editing Models

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Corpora and Translation Education

Part of the book series: New Frontiers in Translation Studies ((NFTS))

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Abstract

Despite the increasingly good quality of automatic translations, machine-translated texts require corrections. Automatic post-editing models have been introduced to perform these corrections without human intervention. However, no system has been able to fully automate the post-editing process. Moreover, while numerous translation tools benefit from translators’ input, human–computer interaction has been underexplored in post-editing. This study discusses automatic post-editing models and suggests that they could be improved in more interactive scenarios, as previously done in machine translation. While some attempts were made to update automatic post-editing models incrementally, this was mostly done using synthetic corpora, which is likely to affect the performance. To address this issue and as part of this project, automatic post-editing models trained in a traditional setting were developed and updated in both batch and online modes without using synthetic resources, with a view to analysing the performance of incremental adaptations in different systems, domains and language pairs. While the interaction with the translator was simulated, an interactive functionality allowing for dynamic post-editing was included for demonstration purposes. The results showed that none of the models was able to beat the baseline and that the online models systematically yielded a lower performance. Moreover, a human evaluation identified recurrent error patterns. These outcomes confirm the difficulties faced by the task of automatic post-editing. Based on the results, several recommendations are put forward for conducting further research, including experiments with more data (possibly synthetic corpora) and different environmental variables.

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Notes

  1. 1.

    WMT: Workshop on Machine Translation. While the research community continued using this acronym, WMT is now a well-established international conference series (Conference on Machine Translation).

  2. 2.

    TF-IDF: Term Frequency-Inverse Document Frequency. This measure was proposed by Salton, et al. (1975) in their theory of term importance, which demonstrated that the relevance of a word is subject not only to its frequency but also its specificity in a document.

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Acknowledgements

We would like to express our sincere gratitude to the volunteers who kindly accepted to work on the evaluation tasks, in particular:

• the English–Spanish team: Lucía Bellés-Calvera (Universitat Jaume I), Rocío Caro Quintana (University of Wolverhampton), Ana Isabel Cespedosa Vázquez (Universidad de Córdoba) and Ana Isabel Martínez Hernández (Universitat Jaume I).

• the German-English team: Anne Eschenbrücher (University of Wolverhampton), Lydia Körber (University of Potsdam, Free University of Berlin) and Alistair Plum (University of Wolverhampton).

• the English-Chinese team: Chien-yu Chen (University of Barcelona), Jacinda Chen (Hong Kong Polytechnic University), Meng Chunyu (Hong Kong Baptist University), Zhujun Zhang (Soochow University), Hellen Zheng (Anastacio Overseas Inc.) and Ruiqi Zhou (Hong Kong Baptist University).

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  1. Universitat Politècnica de València, Valencia, Spain

    Marie Escribe

  2. Lancaster University, Lancaster, England

    Ruslan Mitkov

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  1. Department of Translation, Interpreting and Intercultural Studies, Hong Kong Baptist University, Kowloon, Hong Kong

    Jun Pan

  2. Department of Humanistic Research and Innovation, University of Bari Aldo Moro, Moro, Bari, Italy

    Sara Laviosa

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Escribe, M., Mitkov, R. (2023). Applying Incremental Learning to Post-editing Systems: Towards Online Adaptation for Automatic Post-editing Models. In: Pan, J., Laviosa, S. (eds) Corpora and Translation Education. New Frontiers in Translation Studies. Springer, Singapore. https://doi.org/10.1007/978-981-99-6589-2_3

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