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Editing Taiwan divination Verses with controlled Language Strategies: Machine-Translation-Mediated Effective Communication Chung-ling Shih Abstract Aimed at fostering machine-translation-mediated communication across languages and cultures, this paper proposes editing Taiwan divination verses from the natural into controlled language to improve the comprehensibility of machine translation (MT) outputs. After editing 160 divination verses, and evaluating the semantic and grammatical accuracy of their English MTs that are produced by online Google Translate (a neural MT system for free), the author has identified several controlled language strategies. The lexical strategies include replacement of archaic Chinese words with vernacular Chinese ones, paraphrasing of culture references and insertion of explanations for metaphors. Grammatical strategies are the use of articles, determiners and possessive cases, and syntactical ones include the addition of conjunctions and the restoration of missing subjects or/and objects. The MT outputs of edited and unedited divination verses are compared. The findings show that the English MT outputs of edited texts have greatly improved their semantic, grammatical and syntactic accuracy, so the effectiveness of controlled language strategies is justified. Due to the effectiveness of editing verses with controlled language strategies, the goal of MT-enabled web-based communication across cultures is achieved. The practical significance is also discussed including culture acquisition and cost reduction. Full Text: PDF DOI: 10.15640/jflcc.v8n2a2

With the active participation of users in product review platforms, online consumer-generated content, and, more specifically, user-generated reviews, have become a clear reference in purchasing decision-making processes, which sometimes exceed the impact of advertising campaigns. A common feature of most tourism review platforms is the use of machine translation (MT) systems to immediately make reviews available to users in various languages. However, the quality of the MT output of these reviews varies greatly, primarily due to the subjective and unstructured nature of this digital genre. Different studies confirm that there are no universal quality rating scales. The assessment of MT output quality usually depends on factors such as the purpose of the text or the value given to the immediacy of the translation. New neural MT systems have been a revolution in the quality increase of the translated output; however, new lines of research are opening up to verify whether the quality of this new paradigm of MT can be assessed with the existing scales, mainly from previous rule-based systems and statistical translation, or whether it is necessary to develop new quality metrics specifically for these new intelligent systems. On the other hand, one of the questions that remain to be resolved in this new context of neural MT is whether the use of large amounts of textual data in the training of these systems is as effective as the use of less data but of higher quality and better-adjusted to the specialty and type of text for which it is used. Based on the hypothesis that each genre requires specific quality rating scales, this work identifies the error patterns and textual characteristics of online user reviews from a corpus-based approach analysis that will contribute to adapting quality rating scales to this specific digital genre.

The present paper describes a machine translation (MT) course taught to undergraduate students in the Department of English Language and Literature at Dhofar University in Oman. The course is one of the major requirements for BA in Translation. Fifteen EFL translation students who were in their third year of study were enrolled in the course. The author presents both the theoretical and practical parts of the course. In the theoretical part, the topics covered in the course are outlined. As for the practical part, it focuses on the translation students’ post-editing of online MT output. This is beneficial to the students as free online MT systems can potentially be used as a means for improving student translators’ training and EFL learning. This is achieved through subjecting MT output to analysis or post-editing by the students so that they can focus on the differences between the source and target languages. With this goal in mind, assignments were given to the students to post-edit the Arabic and English MT output of three free online MT systems (Systran, Babylon and Google Translate), discuss the linguistic problems that they spot for each system and choose the one that has the fewest number of errors. The results show that the students, with varying degrees of success, have managed to identify some linguistic errors with the MT output for each MT system and thus produced a better human translation. The paper concludes that there is a need to incorporate MT courses in translation departments in the Arab world, as integrating technology into translation curricula will have great effect on student translators’ training for their future career as professional translators.

Digitalization is one of the key distinctive features of modern environment and social life. Nowadays more and more functions are transferred to the artificial mind. How effective is the replacement of human activity with computer activity? In the given article, this problem is solved by an example of integration of digital technologies into translation activities. It this paper, emphasis is placed on the quality of machine translation (MT) output of legal texts in the language pair English - Slovak. It studies a Criminal Code formulated in the Slovak language which was translated by a human translator into English and consequently via machine translation system Google Translate (GT) back into Slovak. The back-translation - translation of a translated text back into its original language - as a quality assessment tool to detect discrepancies, mistranslations and inevitable differences between the source text and the target text was used. The quality of MT output was evaluated according to Multidimensional Quality Metrics (MQM) standards with the focus on the dimension of Fluency. The multiple comparisons were applied to determine which issues (errors) in Fluency dimension differ from the others. A statistically significant difference is noticed between Agreement and other issues, as well as between Ambiguity and other issues. The errors in Agreement are related to the differences between the languages: English is considered mostly an analytic language, Slovak represents a synthetic language. The issues in the Ambiguity dimension correlate with the type of the text being examined, since legal texts are characterized by relatively complicated wording and numerous terms; moreover, accuracy and unambiguity need to be preserved. Generally, the MT output is able to provide users with basic information about the text. On the other hand, most of the segments need revision and/or correction; in such cases, human intervention and post-editing is necessary.

Compared to fully manual translation, post-editing (PE) machine translation (MT) output can save time and reduce errors. Automatic word-level quality estimation (QE) aims to predict the correctness of words in MT output and holds great promise to aid PE by flagging problematic output. Quality of QE is crucial, as incorrect QE might lead to translators missing errors or wasting time on already correct MT output. Achieving accurate automatic word-level QE is very hard, and it is currently not known (i) at what quality threshold QE is actually beginning to be useful for human PE, and (ii), how to best present word-level QE information to translators. In particular, should word-level QE visualization indicate uncertainty of the QE model or not? In this paper, we address both research questions with real and simulated word-level QE, visualizations, and user studies, where time, subjective ratings, and quality of the final translations are assessed. Results show that current word-level QE models are not yet good enough to support PE. Instead, quality levels of > 80% F1 are required. For helpful quality levels, a visualization reflecting the uncertainty of the QE model is preferred. Our analysis further shows that speed gains achieved through QE are not merely a result of blindly trusting the QE system, but that the quality of the final translations also improves. The threshold results from the paper establish a quality goal for future word-level QE research.

Automatic post-editing (APE) seeks to automatically refine the output of a black-box machine translation (MT) system through human post-edits. APE systems are usually trained by complementing human post-edited data with large, artificial data generated through back-translations, a time-consuming process often no easier than training a MT system from scratch. in this paper, we propose an alternative where we fine-tune pre-trained BERT models on both the encoder and decoder of an APE system, exploring several parameter sharing strategies. By only training on a dataset of 23K sentences for 3 hours on a single GPU we obtain results that are competitive with systems that were trained on 5M artificial sentences. When we add this artificial data our method obtains state-of-the-art results.

Automatic translation of natural language is still (as of 2017) a long-standing but unmet promise. While advancing at a fast rate, the underlying methods are still far from actually being able to reliably capture syntax or semantics of arbitrary utterances of natural language, way off transporting the encoded meaning into a second language. However, it is possible to build useful translating machines when the target domain is well known and the machine is able to learn and adapt efficiently and promptly from new inputs. This is possible thanks to efficient and effective machine learning methods which can be applied to automatic translation. In this work we present and evaluate methods for three distinct scenarios: a) We develop algorithms that can learn from very large amounts of data by exploiting pairwise preferences defined over competing translations, which can be used to make a machine translation system robust to arbitrary texts from varied sources, but also enable it to learn effectively to adapt to new domains of data; b) We describe a method that is able to efficiently learn external models which adhere to fine-grained preferences that are extracted from a constricted selection of translated material, e.g. for adapting to users or groups of users in a computer-aided translation scenario; c) We develop methods for two machine translation paradigms, neural- and traditional statistical machine translation, to directly adapt to user-defined preferences in an interactive post-editing scenario, learning precisely adapted machine translation systems. In all of these settings, we show that machine translation can be made significantly more useful by careful optimization via preference learning.

This work investigates the potential use of post-edited machine translation (MT) outputs as reference translations for automatic machine translation evaluation, focusing mainly on the following important question: Is it necessary to take into account the machine translation system and the source language from which the given post-edits are generated? In order to explore this, we investigated the use of post-edits originating from different machine translation systems (two statistical systems and two rule-based systems), as well as the use of post-edits originating from two different source languages (English and German). The obtained results shown that for comparison of different systems using automatic evaluation metrics, a good option is to use a post-edit originating from a high-quality (possibly distinct) system. A better option is to use it together with other references and post-edits, however post-edits originating from poor translation systems should be avoided. For tuning or development of a particular system, post-edited output of this same system seems to be the best reference translation.

With the advent of the neural paradigm, machine translation has made another leap in quality. As a result, its use by trainee translators has increased considerably, which cannot be disregarded in translation pedagogy. However, since legal texts have features that pose major challenges to machine translation, the question arises as to what extent machine translation is now capable of translating legal texts or at least certain types of legal text into another legal language well enough so that the post-editing effort is limited, and, consequently, whether a targeted use in translation pedagogy can be considered. In order to answer this question, DeepL Translator, a machine translation system, and MateCat, a CAT system that integrates machine translation, were tested. The test, undertaken at different times and without specific translation memories, provided for the translation of several legal texts of different types utilising both systems, and was followed by systematisation of errors and evaluation of translation results. The evaluation was carried out according to the following criteria: 1) comprehensibility and meaningfulness of the target text; and 2) correspondence between source and target text in consideration of the specific translation situation. Overall, the results are considered insufficient to give post-editing of machine-translated legal texts a bigger place in translation pedagogy. As the evaluation of the correspondence between source and target text was fundamentally worse than with regard to the meaningfulness of the target text, translation pedagogy should respond by raising awareness about differences between machine translation output and human translation in this field, and by improving translation approach and strengthening legal expertise.

Problem statement: Any Arabic to English Machine Translation (MT) system should be capable of dealing with word order which Arabic exhibits. This poses a significant challenge to MT due to the vast number of ways to express the same sentence in Arabic. The ordering features are very important and should be carefully applied to ensure the generation of sentence in the target language. Because they apply to the target language, it shoul d fulfill the specific requirement of this language . Mistakes in the MT output can be either the result of analysis problems at the source language level, or due to generation problem at target language level. Word order rules are crucial for the generation of sentences in the target language. They also serve a s rules for the ordering of sentence constituents. These rules draw their information from the syntact ic knowledge. The word order problem becomes more obvious when making machine translation between languages that have rich morphological variations. Approach: The main objective of this research is to develop a machine translation that translates Arabic noun phrases into English by usin g transfer-based approach. A system called Npae- Rbmt has been developed in this research. Transfer- based machine translation is one instance of rule- based machine-translation approaches and is current ly one of the most widely used methods of machine translation. The idea of transfer-based mac hine translation it is necessary to have an intermediate representation that captures the mean ing of the original sentence in order to generate the correct translation. Using advantages of transf er-based machine translation such as analysis step, the Transfer-based becomes simpler as linguistic an alysis goes deeper-as the representation of analysi s step becomes more abstract. In fact, a major goal o f MT research is to define a level of analysis whic h is so deep in which transfer-based machine translat ion is able to do. Results: The method was tested on 88 thesis titles and journals from the computer science domain. The accuracy of the result was 94.6%. These results proved the viability of this a pproach for distant languages. Conclusion: Based on the achieved results, we have managed to perform th e syntactic reordering within an Arabic noun phrases to English translation task by using transf er-based machine translation and also achieved reasonable improvements in translation quality over related approach.

Translation Environment Tools make translators’ work easier by providing them with term lists, translation memories and machine translation output. Ideally, such tools automatically predict whether it is more effortful to post-edit than to translate from scratch, and determine whether or not to provide translators with machine translation output. Current machine translation quality estimation systems heavily rely on automatic metrics, even though they do not accurately capture actual post-editing effort. In addition, these systems do not take translator experience into account, even though novices’ translation processes are different from those of professional translators. In this paper, we report on the impact of machine translation errors on various types of post-editing effort indicators, for professional translators as well as student translators. We compare the impact of MT quality on a product effort indicator (HTER) with that on various process effort indicators. The translation and post-editing process of student translators and professional translators was logged with a combination of keystroke logging and eye-tracking, and the MT output was analyzed with a fine-grained translation quality assessment approach. We find that most post-editing effort indicators (product as well as process) are influenced by machine translation quality, but that different error types affect different post-editing effort indicators, confirming that a more fine-grained MT quality analysis is needed to correctly estimate actual post-editing effort. Coherence, meaning shifts, and structural issues are shown to be good indicators of post-editing effort. The additional impact of experience on these interactions between MT quality and post-editing effort is smaller than expected.

Current machine translation (MT) systems are still not perfect. In practice, the output from these systems needs to be edited to correct errors. A way of increasing the productivity of the whole translation process (MT plus human work) is to incorporate the human correction activities within the translation process itself, thereby shifting the MT paradigm to that of computer-assisted translation. This model entails an iterative process in which the human translator activity is included in the loop: In each iteration, a prefix of the translation is validated (accepted or amended) by the human and the system computes its best (or n-best) translation suffix hypothesis to complete this prefix. A successful framework for MT is the so-called statistical (or pattern recognition) framework. Interestingly, within this framework, the adaptation of MT systems to the interactive scenario affects mainly the search process, allowing a great reuse of successful techniques and models. In this article, alignment templates, phrase-based models, and stochastic finite-state transducers are used to develop computer-assisted translation systems. These systems were assessed in a European project (TransType2) in two real tasks: The translation of printer manuals; manuals and the translation of the Bulletin of the European Union. In each task, the following three pairs of languages were involved (in both translation directions): English-Spanish, English-German, and English-French.

This paper presents the system description of Machine Translation (MT) systems for English-Bangla language pair. Our goal was to create two benchmark MT systems that produce a better quality translation and comparatively higher evaluation score than existing MT systems for English to Bangla. In our experiments, we implemented two baseline MT systems using both statistical and neural methods for the said language pair. Our phrase-based statistical model and 2-layer LSTM neural model were trained and evaluated with a large dataset that is carefully pre-processed and contains unique training data to avoid biases from the cross-validation and test data. We achieved the highest scoring BLEU for our experiments with these setups. Furthermore, we improved the performance of the neural model using pre-trained embedding and synthetic monolingual data which are cutting-edge technology for neural models.

The paper presents a software re-engineering approach that is being used by a consortium of eleven academic institutions facilitated by a software engineering group to re-engineer previously developed natural language processing modules in laboratory environment, for developing eighteen machine translation (MT) systems covering nine pairs of Indic languages. This approach gives field deployable and maintainable MT systems (and modules) that can be reused as customizable components. The paper discusses its symbiotic approach of re-engineering modules, and also the experiences of developing MT systems in consortium mode. Following this approach, already seven MT systems have been developed and deployed on the web.KeywordsMachine TranslationLanguage ResourceLanguage PairSoftware Requirement SpecificationField DeployableThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Advances in information technology and the needs of globalization made English the lingua franca (EFL) of the Web 1.0. Later, multi-modes of computer-mediated communication in the Web 2.0 fostered hybrid English as a new EFL by combing English with other languages. Moving into the Web 3.0 for information mining and knowledge acquisition without a language barrier, the author proposes ‘Webbish’, a controlled form of English, to write web texts for the purpose of gisting through the production of multilingual machine translation (MT) outputs. To demonstrate the effectiveness of ‘Webbish’, an empirical survey was conducted to measure the comprehensibility of the MT of ‘Webbish’ texts based on a 1–100 grading scale. An average score of 89.9 was earned from twenty-four international participants, showing a high degree of MT comprehensibility. Meanwhile, set within the framework of postmodernism, ‘Webbish’ writing takes on postmodernist significance of non-fixed norms, dynamic meanings and an equal right of comprehensible Web information access for global non-English audiences. Its linguistic features are suited to MT application and its fluid meanings are produced due to the MT system’s rendition and different target audiences’ different interpretations. ‘Webbish’ writing suggests acceptance, inclusiveness and equality. It is also an easy way to influence and improve global communication.