Quality assessment of machine translation output

Kasperavičienė, Ramunė; Motiejūnienė, Jurgita; Patašienė, Irena

doi:10.35699/1983-3652.2020.24399

Cited by 7 publications

(7 citation statements)

References 11 publications

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“…This is the author's version which has not been fully edited and content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3366802 [58], [61], [64], [68], [86], [72], [74], [76], [78], [81], [85], and [93] largely focus on various aspects of machine translation, including methodologies, approaches, and applications. In contrast, references [29], [38], [53], [45], [40], [41], [42], [47], [48], [35], [60], [61], [63], [64], [87], [69], [70], [71], [82], and [89] examine the use of deep learning, neural networks, and related technologies in machine translation and natural language processing.…”

Section: Discussionmentioning

confidence: 99%

“…References [1], [3], [11], [30], [31], [51], [32], [39], [55], [72], [73], [78], [81], [85], [88], and [90] specifically examine the assessment of machine translation quality, metrics, and methodologies. References [21], [22], [50], [35], [78], [82], [83], [84], [85], and [91] provide a detailed analysis of machine translation pertaining to particular languages or dialects, including Arabic, Urdu, Sana'ani, and Moroccan Arabic. Furthermore, the evaluation and increase in machine translation quality were examined in [3], [21], [29], [32], [49], [58], [86], [75], [78], [80], [85], [87], and [92].…”

Section: Discussionmentioning

confidence: 99%

“…Liu and Zhu [85] compared and contrasted six popular NMT programs in detail. The major objective of this investigation is to assess how well these tools translate government writing from Chinese to English.…”

Section: Evaluation Metricsmentioning

confidence: 99%

See 2 more Smart Citations

The Impact of Artificial Intelligence on Language Translation: A Review

Mohamed,

Khanan,

Bashir

et al. 2024

IEEE Access

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In the context of a more linked and globalized society, the significance of proficient crosscultural communication has been increasing to a position of utmost importance. Language functions as a crucial medium that establishes connections among people, corporations, and countries, thus demanding the implementation of precise and effective translation systems. This comprehensive review paper aims to contribute to the evolving landscape of AI-driven language translation by critically examining the existing literature, identifying key debates, and uncovering areas of innovation and limitations where the primary objective -is to provide a nuanced understanding of the current state of AI-driven language translation, along with emphasizing the advancements, challenges, and ethical considerations. In this review, ongoing debates surrounding AI-driven language translations were actively involved. By evaluating different viewpoints and methodologies, insights into unresolved questions that contribute to broader discourse in the field were provided. The future trajectory of this study is the incorporation of cross-lingual dialect adaptability and the advancement of Artificial Intelligence translation systems, with a focus on prioritizing inclusion and cultural understanding.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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The Impact of Artificial Intelligence on Language Translation: A Review

Mohamed,

Khanan,

Bashir

et al. 2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…In a study of non-professional users where acceptability of a machine-translated text from English into Lithuanian was tested, an eye tracking experiment revealed that the cognitive processing was greater, i.e., required a longer gaze time and fixation count, on machine translation errors in comparison with correct segments of text (Kasperavičienė et al, 2020 ). The machine-translated segments with errors required more attention and cognitive effort from the readers, but the results regarding overall acceptability of the raw machine-translated text obtained via a post-task survey did not correlate with the readers' gaze time spent on segments with errors.…”

Section: Literature Overviewmentioning

confidence: 99%

Is machine translation a dim technology for its users? An eye tracking study

et al. 2023

Self Cite

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State-of-the-art research shows that the impact of language technologies on public awareness and attitudes toward using machine translation has been changing. As machine translation acceptability is considered to be a multilayered concept, this paper employs criteria of usability, satisfaction and quality as components of acceptability measurement. The study seeks to determine whether there are any differences in the machine-translation acceptability between professional users, i.e., translators and language editors, and non-professional users, i.e., ordinary users of machine translation who use it for non-professional everyday purposes. The main research questions whether non-professional users process raw machine translation output in the same way as professional users and whether there is a difference in the processing of raw machine-translated output between users with different levels of machine-translated text acceptability are analyzed. The results of an eye tracking experiment, measuring fixation time, dwell time and glance count, indicate a difference between professional and non-professional users' cognitive processing and acceptability of machine translation output: translators and language editors spend more time overall reading the machine-translated texts, possibly because of their deeper critical awareness as well as professional attitude toward the text. In terms of acceptability overall, professional translators critically assess machine translation on all components of which confirms the findings of previous similar research. However, the study draws attention to non-professional users' lower awareness regarding machine translation quality. The study was conducted within a research project that received funding from the Research Council of Lithuania (LMTLT, agreement No S-MOD-21-2), seeking to explore and evaluate the impact on society of machine translation technological solutions.

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“…Rimkutė et al (2007) emphasised the importance of MT quality assessment and discussed linguistic peculiarities that arise in the machine translation process. Among the later approaches for English to Lithuanian, machine translation quality evaluation for neural translation systems was conducted by Stankevičiūtė et al (2017) and Kasperavičienė et al (2020). However, one of the most valuable studies on the MT for English-Lithuanian quality evaluation was published by Petkevičiūtė and Tamulynas (2011), who determined the main indicators for translation quality as well as identified many practical translation problems faced by MT systems.…”

Section: Approaches To Machine Translation Quality Assessmentmentioning

confidence: 99%

Machine Translation Quality in Mobile Apps for Text-based Image Translation

Miltakienė

2021

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With the advancement of mobile applications, now it is possible to perform instant text translation using a smartphone’s camera. Because text translation within images is still a relatively new field of research, it is not surprising that the translation quality of these mobile applications is under-researched. This study aims to determine the image-to-text translation quality in the English to Lithuanian language direction using popular machine translation apps. To classify errors and evaluate the quality of translation, the present study adopts and customizes the Multidimensional Quality Metrics (MQM) framework (Lommel 2014). The obtained results indicate that image-to-text machine translation apps produce exceptionally low-quality translations for the English-Lithuanian language pair. Therefore, the quality of machine translation for low-resource languages such as Lithuanian remains an issue.

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Quality assessment of machine translation output

Cited by 7 publications

References 11 publications

The Impact of Artificial Intelligence on Language Translation: A Review

The Impact of Artificial Intelligence on Language Translation: A Review

Is machine translation a dim technology for its users? An eye tracking study

Machine Translation Quality in Mobile Apps for Text-based Image Translation

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