Catalogue Search | MBRL
Are you sure you want to remove the book from the shelf?
{{itemTitle}}
97
result(s) for
"Translating and interpreting Dictionaries"
Sort by:
Dictionary of Education and Assessment in Translation and Interpreting Studies (TIS)
This book is the first and only dictionary on education and assessment in the context of translator and interpreter training. It offers the reader in-depth and up-to-date knowledge regarding key issues of the education and assessment of translators and interpreters, including how best to train translators and interpreters and how best to assess their performance in pedagogical settings. It contains key terms defined and discussed with a broad focus, and arranged alphabetically. It will serve as a valuable resource for academic researchers, educators, and assessors in translation and interpreting studies, as well as practitioners and students of translation and interpreting studies.
eBook
Nation, Language, and the Ethics of Translation
In recent years, scholarship on translation has moved well beyond the technicalities of converting one language into another and beyond conventional translation theory. With new technologies blurring distinctions between \"the original\" and its reproductions, and with globalization redefining national and cultural boundaries, \"translation\" is now emerging as a reformulated subject of lively, interdisciplinary debate. Nation, Language, and the Ethics of Translation enters the heart of this debate. It covers an exceptional range of topics, from simultaneous translation to legal theory, from the language of exile to the language of new nations, from the press to the cinema; and cultures and languages from contemporary Bengal to ancient Japan, from translations of Homer to the work of Don DeLillo.
All twenty-two essays, by leading voices including Gayatri Spivak and the late Edward Said, are provocative and persuasive. The book's four sections--\"Translation as Medium and across Media,\" \"The Ethics of Translation,\" \"Translation and Difference,\" and \"Beyond the Nation\"--together provide a comprehensive view of current thinking on nationality and translation, one that will be widely consulted for years to come.
The contributors are Jonathan E. Abel, Emily Apter, Sandra Bermann, Vilashini Cooppan, Stanley Corngold, David Damrosch, Robert Eaglestone, Stathis Gourgouris, Pierre Legrand, Jacques Lezra, Françoise Lionnet, Sylvia Molloy, Yopie Prins, Edward Said, Azade Seyhan, Gayatri Chakravorty Spivak, Henry Staten, Lawrence Venuti, Lynn Visson, Gauri Viswanathan, Samuel Weber, and Michael Wood.
eBook
Semantic alignment: A measure to quantify the degree of semantic equivalence for English–Chinese translation equivalents based on distributional semantics
The degree of semantic equivalence of translation pairs is typically measured by asking bilinguals to rate the semantic similarity of them or comparing the number and meaning of dictionary entries. Such measures are subjective, labor-intensive, and unable to capture the fine-grained variation in the degree of semantic equivalence. Thompson et al. (in
Nature Human Behaviour
,
4
(10), 1029–1038,
2020
) propose a computational method to quantify the extent to which translation equivalents are semantically aligned by measuring the contextual use across languages. Here, we refine this method to quantify semantic alignment of English–Chinese translation equivalents using word2vec based on the proposal that the degree of similarity between the contexts associated with a word and those of its multiple translations vary continuously. We validate our measure using semantic alignment from GloVe and fastText, and data from two behavioral datasets. The consistency of semantic alignment induced across different models confirms the robustness of our method. We demonstrate that semantic alignment not only reflects human semantic similarity judgment of translation equivalents but also captures bilinguals’ usage frequency of translations. We also show that our method is more cognitively plausible than Thompson et al.’s method. Furthermore, the correlations between semantic alignment and key psycholinguistic factors mirror those between human-rated semantic similarity and these variables, indicating that computed semantic alignment reflects the degree of semantic overlap of translation equivalents in the bilingual mental lexicon. We further provide the largest English–Chinese translation equivalent dataset to date, encompassing 50,088 translation pairs for 15,734 English words, their dominant Chinese translation equivalents, and their semantic alignment
Rc
values.
Journal Article
Design and Implementation of Chinese Common Braille Translation System Integrating Braille Word Segmentation and Concatenation Rules
An important sign of the accessibility of Braille information is the realization of the mutual translation between Chinese and the Braille. Due to the irregularity and uncertainty of the Prevailing Mandarin Braille, coupled with the lack of a large-scale Braille corpus, the quality of Chinese-Braille translation seems to be poor. In July 2018, the National Language Commission released the “Chinese Common Braille Scheme” and advocated replacing the “Prevailing Mandarin Braille.” Aimed at improving translation accuracy, this research, which is based on the self-built Chinese Common Braille corpus and combined with the HanLP (Han Language Processing) dictionary and the Chinese-Braille word corpus (a Braille word segmentation and concatenation dictionary for generating a unigram language model), uses the n-gram language model to design and implement a Chinese-Braille intertranslation system that integrates Chinese and Braille Word Segmentation and Concatenation Rules. More importantly, this research proposes an experimental plan for improving the Braille Word Segmentation and Concatenation Rules using a Chinese-Braille word corpus. Experiments show that in the field of educational literature, the accuracy rate of translation from Chinese to Chinese Common Braille has reached 95.01%, and the accuracy of Chinese Common Braille to Chinese translation has reached 90.15%.
Journal Article
Compilation of Dictionaries and Scientific and Technological Translations by Western Protestant Missionaries in China in the Nineteenth Century
The 19th century saw the important transformation of modern Western concepts into Chinese lexical resources. The missionaries were the initiators and important driving force for the translation of Western books into Chinese in modern China. They promoted ‘translating terms’ and ‘coining terms’ in their translations of Western books and the compilation of dictionaries with the cooperation of Chinese intellectuals. Their work provided a tangible ‘word’ carrier of ‘concepts’ for disseminating modern knowledge from the West to the East. Compiled by missionaries, the English–Chinese bilingual dictionaries introduced a brand-new concept of dictionary compilation and changed China’s history of having zidian (字典, character dictionaries) but no cidian (辞典, specialized dictionaries). In particular, John Fryer applied the translation method of creating new words or characters in the translation of chemical terminology. Members of the School and Textbook Series Committee, including John Fryer and Calvin Wilson Mateer, made great contributions to theories and strategies for translation, which keep inspiring Chinese–English translation of terminology and its theoretical construction.
Journal Article
The Use of Large Language Models for Translating Buddhist Texts from Classical Chinese to Modern English: An Analysis and Evaluation with ChatGPT 4, ERNIE Bot 4, and Gemini Advanced
This study conducts a comprehensive evaluation of large language models (LLMs), including ChatGPT 4, ERNIE Bot 4, and Gemini Advanced, in the context of translating Buddhist texts from classical Chinese to modern English. Focusing on three distinct Buddhist texts encompassing various literary forms and complexities, the analysis examines the models’ capabilities in handling specialized Buddhist terminology, classical Chinese grammar, and the translation of complex, lengthy sentences. The study employs a methodology where selected excerpts from these texts are translated by the LLMs, followed by an in-depth analysis comparing these machine-generated translations to human translations. The evaluation criteria include word translation accuracy, the ability to recognize and correctly interpret specific meanings within both classical and modern contexts, and the completeness of phrases without omitting or unnecessarily adding words. The findings reveal significant variations in the performance of these LLMs, with detailed observations on their strengths and weaknesses in translating specialized terms, managing grammatical structures unique to classical Chinese, and maintaining the integrity of the original texts’ meanings. This paper aims to shed light on the potential and limitations of using LLMs for translating complex literary works from ancient to modern languages, contributing valuable insights into the field of computational linguistics and the ongoing development of translation technologies.
Journal Article
Translation Performance from the User’s Perspective of Large Language Models and Neural Machine Translation Systems
The rapid global expansion of ChatGPT, which plays a crucial role in interactive knowledge sharing and translation, underscores the importance of comparative performance assessments in artificial intelligence (AI) technology. This study concentrated on this crucial issue by exploring and contrasting the translation performances of large language models (LLMs) and neural machine translation (NMT) systems. For this aim, the APIs of Google Translate, Microsoft Translator, and OpenAI’s ChatGPT were utilized, leveraging parallel corpora from the Workshop on Machine Translation (WMT) 2018 and 2020 benchmarks. By applying recognized evaluation metrics such as BLEU, chrF, and TER, a comprehensive performance analysis across a variety of language pairs, translation directions, and reference token sizes was conducted. The findings reveal that while Google Translate and Microsoft Translator generally surpass ChatGPT in terms of their BLEU, chrF, and TER scores, ChatGPT exhibits superior performance in specific language pairs. Translations from non-English to English consistently yielded better results across all three systems compared with translations from English to non-English. Significantly, an improvement in translation system performance was observed as the token size increased, hinting at the potential benefits of training models on larger token sizes.
Journal Article
Four hundred Greek idiomatic expressions: Ratings for subjective frequency, ambiguity, and decomposability
Idioms differ from other forms of figurative language because of their dimensions of subjective frequency, ambiguity (possibility of having a literal interpretation), and decomposability (possibility of the idiom’s words to assist in its figurative interpretation). This study focuses on the Greek language and aims at providing the first corpus of 400 Greek idioms rated for their dimensions by 113 native Greek students, aged 19 to 39 years. The study aimed at (1) rating all idioms for their degree of subjective frequency, ambiguity, and decomposability, and (2) investigating the relationships between these dimensions. Three different assessments were conducted, during which the participants were asked to evaluate the degree of idioms’ subjective frequency, ambiguity, and decomposability. The idioms were selected from a dictionary of Greek idioms titled “Dictionary of Idioms in Modern Greek” (Vlaxopoulos, 2007). This study resulted in the first database of Greek idioms assessed for their dimensions. The intraclass correlation coefficient (ICC) (two-way mixed, absolute agreement) demonstrated high internal consistency in the ratings given for each dimension, for the same idiom, by the different individual raters. Correlational analyses showed that subjective frequency was positively moderately correlated with decomposability, and positively weakly correlated with ambiguity, while decomposability was positively moderately correlated with ambiguity.
Journal Article
Machine Translation in the Era of Large Language Models:A Survey of Historical and Emerging Problems
Historically regarded as one of the most challenging tasks presented to achieve complete artificial intelligence (AI), machine translation (MT) research has seen continuous devotion over the past decade, resulting in cutting-edge architectures for the modeling of sequential information. While the majority of statistical models traditionally relied on the idea of learning from parallel translation examples, recent research exploring self-supervised and multi-task learning methods extended the capabilities of MT models, eventually allowing the creation of general-purpose large language models (LLMs). In addition to versatility in providing translations useful across languages and domains, LLMs can in principle perform any natural language processing (NLP) task given sufficient amount of task-specific examples. While LLMs now reach a point where they can both replace and augment traditional MT models, the extent of their advantages and the ways in which they leverage translation capabilities across multilingual NLP tasks remains a wide area for exploration. In this literature survey, we present an introduction to the current position of MT research with a historical look at different modeling approaches to MT, how these might be advantageous for the solution of particular problems, and which problems are solved or remain open in regard to recent developments. We also discuss the connection of MT models leading to the development of prominent LLM architectures, how they continue to support LLM performance across different tasks by providing a means for cross-lingual knowledge transfer, and the redefinition of the task with the possibilities that LLM technology brings.
Journal Article