Recent Advances in Dialogue Machine Translation

Liu, Siyou; Sun, Yuqi; Wang, Longyue

doi:10.3390/info12110484

Cited by 3 publications

(2 citation statements)

References 54 publications

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“…It can be a complex process, since it commonly requires not only expert knowledge of 2 different languages, but also of different cultural milieus. Digital tools that can assist with translation are becoming more ubiquitous every year, tied to advances in fields like optical character recognition (OCR) and machine translation ( 1 , 2 ). Ancient languages, however, still pose a towering problem in this regard.…”

Section: Introductionmentioning

confidence: 99%

Translating Akkadian to English with neural machine translation

et al. 2023

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Cuneiform is one of the earliest writing systems in recorded human history (ca. 3,400 BCE–75 CE). Hundreds of thousands of such texts were found over the last two centuries, most of which are written in Sumerian and Akkadian. We show the high potential in assisting scholars and interested laypeople alike, by using natural language processing (NLP) methods such as convolutional neural networks (CNN), to automatically translate Akkadian from cuneiform Unicode glyphs directly to English (C2E) and from transliteration to English (T2E). We show that high-quality translations can be obtained when translating directly from cuneiform to English, as we get 36.52 and 37.47 Best Bilingual Evaluation Understudy 4 (BLEU4) scores for C2E and T2E, respectively. For C2E, our model is better than the translation memory baseline in 9.43, and for T2E, the difference is even higher and stands at 13.96. The model achieves best results in short- and medium-length sentences (c. 118 or less characters). As the number of digitized texts grows, the model can be improved by further training as part of a human-in-the-loop system which corrects the results.

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Section: Introductionmentioning

confidence: 99%

Translating Akkadian to English with neural machine translation

et al. 2023

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“…Today, machine translation plays an important role in how people communicate, work, travel, learn, access information, and more throughout the world (Lee, 2020;Khoong & Rodriguez, 2022). The term Machine Translation (MT) can be defined as computerized systems that produce the most similar meaning of the source language with or without human assistance (Gupta & Thakral, 2019;Kumar et al, 2019;Liu, Sun, & Wang, 2021). Both SL and TL users can choose their preferred language.…”

Section: Introductionmentioning

confidence: 99%

Unveiling Semantic Errors Found in Lexical Translations of Tasya Farasya’s Tiktok Account

Utami,

Utami

2023

Lingua Cultura

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The research observed the semantic errors in lexis that occurred in the translation by TikTok machine translation. It became the main issue in translation studies because the accuracy of translation produced by machine translation was still questionable and debatable. The research aimed to identify the types of semantic error in lexis made by TikTok auto machine translation found in Tasya Farasya TikTok’s account and suggested a more appropriate translation. The research applied a descriptive qualitative method to analyze the error in translation produced by TikTok machine translation. The theory proposed by Sayogie (2014) was used to classify the data based on semantic aspects: grammatical meaning, contextual meaning, and referential meaning. The research results show that three types of errors are found, and the most frequent error found is an error in contextual meaning. TikTok machine translation is incapable of translating accurately because it does not know the context of the situation and translates it literally. Based on research findings, TikTok users cannot entirely rely on machine translation because it still has weaknesses in translating several terms. Thus, it is highly important that TikTok should evaluate and improve the quality of the machine translation.

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Low-Resource Neural Machine Translation: A Systematic Literature Review

Yazar,

Şahın,

Kiliç

2023

IEEE Access

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In this study, a systematic literature review was conducted to examine the significant works in the literature on low-resource neural machine translation. Within the scope of the study, three research questions were identified to examine the low-resource neural machine translation literature. According to the inclusion and exclusion criteria, 45 studies were selected for review. After the relevant studies were identified, three research questions were aimed to be answered. The first research question is to identify the study directions and language pairs used in low-resource neural machine translation. The second research question aims to identify which deep learning methods are used in low-resource neural machine translation and which metrics are used to evaluate these methods. The third research question is to determine the bilingual and monolingual corpora used in the studies and the preferred development environments. In addition, the studies with the most commonly used language pairs were analyzed, and directions for future studies were made.

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Recent Advances in Dialogue Machine Translation

Cited by 3 publications

References 54 publications

Translating Akkadian to English with neural machine translation

Translating Akkadian to English with neural machine translation

Unveiling Semantic Errors Found in Lexical Translations of Tasya Farasya’s Tiktok Account

Low-Resource Neural Machine Translation: A Systematic Literature Review

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