Towards the evaluation of automatic simultaneous speech translation from a communicative perspective

Fantinuoli, Claudio; Prandi, Bianca

doi:10.48550/arxiv.2103.08364

Cited by 3 publications

(4 citation statements)

References 25 publications

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“…In Eqn (3), 𝑤 𝑖 signifies the 𝑖-th particle's prevailing status, 𝑢 𝑖 for the current particle speed, 𝑜𝐵𝑒𝑠𝑡 𝑗 for the 𝑗 th particle's most effective solutions, 𝑏𝑒𝑠𝑡 for the swarm's historical fiction most effective solution, 𝑑 for scale, 𝑟𝑎𝑛𝑑 1 𝑐 ∈ [0,1] and 𝑟𝑎𝑛𝑑 2 𝑐 ∈ [0,1]; for the swarm as a whole, and d1 and d2 to indicate learning factors. A single particle's speed modification is seen in Eqn (4).…”

Section: Pso Algorithmmentioning

confidence: 99%

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An Intelligent Evaluation Method of Business English Translation Ability Based on Collaborative Game Particle Swarm Algorithm

Yu,

Zhou,

Zhang

2024

Preprint

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In today's globalized business climate, translation is essential for promoting successful communication. For precise and dependable communication, it is crucial to assess the translation skills of experts in the area of business English. However, the current evaluation techniques frequently lack clever and team-based methodologies to evaluate translation talents completely. In order to fill this gap, the Collaborative Game Particle Swarm Algorithm (CGPSA) is used in this study to present an intelligent evaluation approach for Business English translation proficiency. The CGPSA develops an interesting and efficient evaluation procedure by fusing the ideas of game theory and particle swarm optimization (PSO). The development of computerized sentence recognition that is specifically adapted to the features of business English has been greatly aided by the CGPSA. The CGPSA has been used to collect, organize, and analyze data, enabling sophisticated identification of business English. With a total score of 93.4, the suggested framework's results show high recognition accuracy. The findings show that in terms of accuracy in recognition, frequency magnitude, and intelligent translation, the CGPSA-based assessment technique performs better than conventional evaluation methodologies.

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Section: Pso Algorithmmentioning

confidence: 99%

“…The intricacies, traditions, and manners of both the sending and receiving customs must be understood by a qualified business translation. The capacity of the translator to modify the translation while keeping the original message and accent should be evaluated [4]. When translating for business, reliability is crucial.…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Evaluation Method of Business English Translation Ability Based on Collaborative Game Particle Swarm Algorithm

Yu,

Zhou,

Zhang

2024

Preprint

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show abstract

“…The analysis of quality often relies on a manual evaluation of the corpus data (Fantinuoli and Prandi, 2021). Ino and Kawahara (2008), for example, investigated SI faithfulness based on manual annotation of the data.…”

Section: Translation Studiesmentioning

confidence: 99%

Large-Scale English-Japanese Simultaneous Interpretation Corpus: Construction and Analyses with Sentence-Aligned Data

Doi¹,

Sudoh²,

Nakamura³

2021

Proceedings of the 18th International Conference on Spoken Language Translation (IWSLT 2021)

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This paper describes the construction of a new large-scale English-Japanese Simultaneous Interpretation (SI) corpus and presents the results of its analysis. A portion of the corpus contains SI data from three interpreters with different amounts of experience. Some of the SI data were manually aligned with the source speeches at the sentence level. Their latency, quality, and word order aspects were compared among the SI data themselves as well as against offline translations. The results showed that (1) interpreters with more experience controlled the latency and quality better, and (2) large latency hurt the SI quality.

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“…Previous studies have investigated various aspects of AI translation for literature, such as evaluating the quality of output using customized rubrics (Fantinuoli & Prandi, 2021), enhancing the training of neural networks using stylistically annotated parallel corpora (Cipriani, 2023), and promoting interdisciplinary collaboration between literary scholarship and computer science (Goto & Tanaka, 2017). However, there is a lack of comprehensive and updated comparative analysis of leading AI translation platforms on literary texts, especially on rhetorical devices like metaphors, rhyme, and imagery, which are essential for conveying the author's intended meaning and effect.…”

Section: Introductionmentioning

confidence: 99%

Evaluating machine translation of literature through rhetorical analysis

Karabayeva,

Kalizhanova

2024

JTLS

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This paper looks at how well ChatGPT and DeepL, two AI tools, translate literary works. Not only can ChatGPT translate text, but it can also carry out other jobs. DeepL is a service that performs computer translation and uses neural networks. The paper looks at how ChatGPT and DeepL translate books, poems, and dialogues compared to translations done by humans. The paper also talks about the pros and cons of using machine translation for literary reasons, including issues of creativity, style, and adapting to different cultures. The paper uses both new and old studies on machine translation technologies and how they work with human translation. The paper comes to the conclusion that ChatGPT and DeepL are useful but imperfect tools for translating literature, and they require human review and improvement. The paper adds to the fields of machine translation and natural language processing by looking at how two cutting-edge AI tools, ChatGPT and DeepL, can be used to translate literary works. The paper also adds to literature studies and digital humanities by looking into what machine translation can and can't do for creative writing and dialog systems. The goal of the paper is to encourage researchers, translators, writers, and users from different fields to work together and talk to each other. ⁤

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Towards the evaluation of automatic simultaneous speech translation from a communicative perspective

Cited by 3 publications

References 25 publications

An Intelligent Evaluation Method of Business English Translation Ability Based on Collaborative Game Particle Swarm Algorithm

An Intelligent Evaluation Method of Business English Translation Ability Based on Collaborative Game Particle Swarm Algorithm

Large-Scale English-Japanese Simultaneous Interpretation Corpus: Construction and Analyses with Sentence-Aligned Data

Evaluating machine translation of literature through rhetorical analysis

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