A Hybrid Neural Machine Translation Technique for Translating Low Resource Languages

Almansor, Ebtesam Hussain; Al-Ani, Ahmed

doi:10.1007/978-3-319-96133-0_26

Cited by 17 publications

(9 citation statements)

References 9 publications

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“…Third, the current study focuses on human translation. Considering the recent success of neural machine translation ( Almansor and Al-Ani, 2018 ; Islam et al, 2021 ), it will contribute further to translation performance research if different task types (i.e., human translation, and post-editing of neural machine translation) are taken into account. Future studies could diversify the design of task features (e.g., task type) and select participants with different language pairs and diverse education backgrounds, so as to explore further the relationships between variables in task complexity, learner factors, and translation performance with larger samples.…”

Section: Discussionmentioning

confidence: 99%

The impact of task complexity and translating self-efficacy belief on students’ translation performance: Evidence from process and product data

2022

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Previous studies that explored the impact of task-related variables on translation performance focused on task complexity but reported inconsistent findings. This study shows that, to understand the effect of task complexity on translation process and its end product, performance in translation tasks of various complexity levels needs to be compared in a specific setting, in which more factors are considered besides task complexity—especially students’ translating self-efficacy belief (TSEB). Data obtained from screen recording, subjective rating, semi-structured interview, and quality evaluation were triangulated to measure how task complexity influenced the translation performance of Chinese students with high and low TSEB. We found that the complex task led to significantly longer task duration, greater self-reported cognitive effort, lower accuracy, and poorer fluency than the simple one among students, irrespective of their TSEB level. Besides, the high-TSEB group outperformed the low-TSEB group in translation accuracy and fluency in both tasks. However, the interaction effect of task complexity and TSEB was not significant, due possibly to weak problem awareness among students. Our study has implications for effectively designing task complexity, getting the benefits of TSEB, and improving research on translation performance.

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Section: Discussionmentioning

confidence: 99%

The impact of task complexity and translating self-efficacy belief on students’ translation performance: Evidence from process and product data

2022

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“…Almansor and Al-Ani [70] presented a character-based hybrid NMT model that combines both RNN and CNN networks. They trained their model on a very small portion of the TED parallel corpora containing only 90K sentence pairs, notably IWSLT 2016 Arabic-English.…”

Section: ) Low-resourcementioning

confidence: 99%

Arabic Machine Translation: A Survey With Challenges and Future Directions

et al. 2021

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“…Our second finding tells us the capability of MMPLMs in generating a new language pair knowledge space for translating clinical domain text even though this language pair was unseen in the pre-training stage with our experimental settings. This can be useful to low-resource NLP, such as the work by ( 26 , 27 ). 2…”

Section: Introductionmentioning

confidence: 99%

Neural machine translation of clinical text: an empirical investigation into multilingual pre-trained language models and transfer-learning

Han,

Gladkoff,

Erofeev

et al. 2024

Front. Digit. Health

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Clinical text and documents contain very rich information and knowledge in healthcare, and their processing using state-of-the-art language technology becomes very important for building intelligent systems for supporting healthcare and social good. This processing includes creating language understanding models and translating resources into other natural languages to share domain-specific cross-lingual knowledge. In this work, we conduct investigations on clinical text machine translation by examining multilingual neural network models using deep learning such as Transformer based structures. Furthermore, to address the language resource imbalance issue, we also carry out experiments using a transfer learning methodology based on massive multilingual pre-trained language models (MMPLMs). The experimental results on three sub-tasks including (1) clinical case (CC), (2) clinical terminology (CT), and (3) ontological concept (OC) show that our models achieved top-level performances in the ClinSpEn-2022 shared task on English-Spanish clinical domain data. Furthermore, our expert-based human evaluations demonstrate that the small-sized pre-trained language model (PLM) outperformed the other two extra-large language models by a large margin in the clinical domain fine-tuning, which finding was never reported in the field. Finally, the transfer learning method works well in our experimental setting using the WMT21fb model to accommodate a new language space Spanish that was not seen at the pre-training stage within WMT21fb itself, which deserves more exploitation for clinical knowledge transformation, e.g. to investigate into more languages. These research findings can shed some light on domain-specific machine translation development, especially in clinical and healthcare fields. Further research projects can be carried out based on our work to improve healthcare text analytics and knowledge transformation. Our data is openly available for research purposes at: https://github.com/HECTA-UoM/ClinicalNMT.

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A Hybrid Neural Machine Translation Technique for Translating Low Resource Languages

Cited by 17 publications

References 9 publications

The impact of task complexity and translating self-efficacy belief on students’ translation performance: Evidence from process and product data

The impact of task complexity and translating self-efficacy belief on students’ translation performance: Evidence from process and product data

Arabic Machine Translation: A Survey With Challenges and Future Directions

Neural machine translation of clinical text: an empirical investigation into multilingual pre-trained language models and transfer-learning

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