Towards a corpus-based, statistical approach of translation quality : measuring and visualizing linguistic deviance in student translations

In this article we present a corpus-based statistical approach to measuring translation quality, more particularly translation acceptability, by comparing the features of translated and original texts. We discuss initial findings that aim to support and objectify formative quality assessment. To that end, we extract a multitude of linguistic and textual features from both student and professional translation corpora that consist of many different translations by several translators in two different genres (fiction, news) and in two translation directions (English to French and French to Dutch). The numerical information gathered from these corpora is exploratively analysed with Principal Component Analysis, which enables us to identify stable, language-independent linguistic and textual indicators of student translations compared to translations produced by professionals. The differences between these types of translation are subsequently tested by means of ANOVA. The results clearly indicate that the proposed methodology is indeed capable of distinguishing between student and professional translations. It is claimed that this deviant behaviour indicates an overall lower translation quality in student translations: student translations tend to score lower at the acceptability level, that is, they deviate significantly from target-language norms and conventions. In addition, the proposed methodology is capable of assessing the acceptability of an individual student’s translation – a smaller linguistic distance between a given student translation and the norm set by the professional translations correlates with higher quality. The methodology is also able to provide objective and concrete feedback about the divergent linguistic dimensions in their text

Machine Learning for Readability Assessment and Text Simplification in Crisis Communication: A Systematic Review

In times of social media, crisis managers can interact with the citizens in a variety of ways. Since machine learning has already been used to classify messages from the population, the question is, whether such technologies can play a role in the creation of messages from crisis managers to the population. This paper focuses on an explorative research revolving around selected machine learning solutions for crisis communication. We present systematic literature reviews of readability assessment and text simplification. Our research suggests that readability assessment has the potential for an effective use in crisis communication, but there is a lack of sufficient training data. This also applies to text simplification, where an exact assessment is only partly possible due to unreliable or non-existent training data and validation measures

Unraveling the Relationship between Content Design and Kinesthetic Learning on Communities of Practice Platforms

As a variant of the sharing economy, Communities of Practice (CoP) platforms have allowed kinesthetic learners to acquire skillsets corresponding to their interests for immediate or future use in practice. However, the impact of digital learning content design on kinesthetic learning remains underexplored in the field of information systems. We hence extend prior research by advancing content richness and structure clarity as antecedents affecting kinesthetic learners’ digestibility of contents, culminating in differential kinesthetic learning effects. To substantiate our arguments, we collected data from a leading Chinese recipe sharing platform. Whereas content richness was measured in terms of readability, verb richness, and prototypicality, structure clarity was operationalized as block structure, block quantity, and block regularity. Employing a machine learning model, we simulated and tested learners’ digestibility of image content embodied within recipes. Plans for future research beyond the current study are also discussed

A Comparative Study of Linguistic Features of English Agricultural Journal Abstracts Written by American and Chinese Scientists

The present study investigated the variations in linguistic features of English academic writing by American and Chinese scientists by building a corpus of 600 English agricultural journal abstracts and using the natural language processing tool Coh-Metrix. Through a one-way Analysis of Variance (ANOVA) and a discriminant function analysis (DFA), we statistically analyzed the corpus texts based on their lexical, syntactic and cohesive features and generated 8 distinguishing linguistic indices. The results indicated that Chinese scientists tended to write abstracts with more frequent words, more similar sentence structures, more modifiers per noun phrase and more agentless passive voice forms, while the American counterparts tended to write abstracts with a wider range of vocabulary, more specific terms, more words with multiple senses and more adversative connectives. These findings offer good guidance for Chinese scientists to write in a style closer to the agricultural research field and the native speakers so as to get their manuscripts better reviewed and more easily published. These findings also have practical implications for the development of agricultural English teaching materials as well as the curriculum design

Exploring the effectiveness of ChatGPT-based feedback compared with teacher feedback and self-feedback: Evidence from Chinese to English translation

ChatGPT,a cutting-edge AI-powered Chatbot,can quickly generate responses on given commands. While it was reported that ChatGPT had the capacity to deliver useful feedback, it is still unclear about its effectiveness compared with conventional feedback approaches,such as teacher feedback (TF) and self-feedback (SF). To address this issue, this study compared the revised Chinese to English translation texts produced by Chinese Master of Translation and Interpretation (MTI) students,who learned English as a Second/Foreign Language (ESL/EFL), based on three feedback types (i.e., ChatGPT-based feedback, TF and SF). The data was analyzed using BLEU score to gauge the overall translation quality as well as Coh-Metrix to examine linguistic features across three dimensions: lexicon, syntax, and cohesion.The findings revealed that TF- and SF-guided translation texts surpassed those with ChatGPT-based feedback, as indicated by the BLEU score. In terms of linguistic features,ChatGPT-based feedback demonstrated superiority, particularly in enhancing lexical capability and referential cohesion in the translation texts. However, TF and SF proved more effective in developing syntax-related skills,as it addressed instances of incorrect usage of the passive voice. These diverse outcomes indicate ChatGPT's potential as a supplementary resource, complementing traditional teacher-led methods in translation practice

Readings on L2 reading: Publications in other venues 2021-2022

This feature offers an archive of articles published in other venues during the past year and serves as a valuable tool to readers of Reading in a Foreign Language (RFL). It treats any topic within the scope of RFL and second language reading. The articles are listed in alphabetical order, each with a complete reference as well as a brief summary. The editors of this feature attempt to include all related articles that appear in other venues. However, undoubtedly, this list is not exhaustive

Readings on L2 reading: Publications in other venues 2015–2016

This feature offers an archive of articles published in other venues during the past year and serves as a valuable tool to readers of Reading in a Foreign Language (RFL). It treats any topic within the scope of RFL and second language reading. The articles are listed in alphabetical order, each with a complete reference as well as a brief summary. The editors of this feature attempt to include all related articles that appear in other venues. However, undoubtedly, this list is not exhaustive

Development of an international written communication audit

Although localization, internationalization, and globalization efforts to meet international customers\u27 product and information needs are accepted strategies in the US computer industry, the needs of second language (L2) English speakers are less directly addressed in the international workplace. Application of strategies similar to these three technical communication strategies may benefit international workplace communication;The international writing approaches represented by these three communication strategies are related to the global management strategies of organizations (e.g., ethnocentric, polycentric, geocentric). This categorization, based on Perlmutter and Hedlund, considers organizations\u27 strategic missions and can be used to align management strategies with international writing approaches and individual rhetorical strategies. For example, an ethnocentric organization, entering the international market from a broad national base, instead of immediately changing its communication approach, might continue to use its source-localized information to communicate internationally. An organization might enter the global arena with an ethnocentric strategy, and, in reaction to emerging problems, focus on localization for each market and rely heavily on translation and translators, becoming more polycentric in its approach. A geocentric organization, balancing between ethnocentric and polycentric management strategies, is in constant communication across national and language borders, and might use both internationalization and globalization approaches in communication. Organizations\u27 global management strategies should align with their international communication practices, both for customers and in the workplace. An organization seeking a larger role in international ventures, yet with ethnocentric, localized communication strategies, might be less successful than one with similar goals and a more geocentric, globalized communication;In recognition of the diverse needs of organizations and individuals, an assessment method, an International Written Communication Audit (IWCA), is developed in this dissertation. The IWCA, based on linguistic and contrastive rhetoric research, focuses on cultural, pragmatic, and translation issues important to international workplace writing in US-English. The basic IWCA combines internationalization and globalization approaches. A localization module for the PRC is offered as an example of tailoring the audit methodology to the needs of L2 English readers from a specific language group. The construction of a workplace sampling frame and the analysis of the IWCA data are discussed