The Translator’s Extended Mind

The rapid development of natural language processing in the last three decades has drastically changed the way professional translators do their work. Nowadays most of them use computer-assisted translation (CAT) or translation memory (TM) tools whose evolution has been overshadowed by the much more sensational development of machine translation (MT) systems, with which TM tools are sometimes confused. These two language technologies now interact in mutually enhancing ways, and their increasing role in human translation has become a subject of behavioral studies. Philosophers and linguists, however, have been slow in coming to grips with these important developments. The present paper seeks to fill in this lacuna. I focus on the semantic aspects of the highly distributed human–computer interaction in the CAT process which presents an interesting case of an extended cognitive system involving a human translator, a TM tool, an MT engine, and sometimes other human translators or editors. Considered as a whole, such a system is engaged in representing the linguistic meaning of the source document in the target language. But the roles played by its various components, natural as well as artificial, are far from trivial, and the division of linguistic labor between them throws new light on the familiar notions that were initially inspired by rather different phenomena in the philosophy of language, mind, and cognitive science.

     

A process study of computer-aided translation

We investigate novel types of assistance for human translators, based on statistical machine translation methods. We developed the computer-aided tool Caitra that makes suggestions for sentence completion, shows word and phrase translation options, and allows postediting of machine translation output. We carried out a study of the translation process that involved non-professional translators that were native in either French or English and recorded their interaction with the tool. Users translated 192 sentences from French news stories into English. Most translators were faster and better when assisted by our tool. A detailed examination of the logs also provides insight into the human translation process, such as time spent on different activities and length of pauses.     

Power shifts in web-based translation memory

Web-based translation memory (TM) is a recent and little-studied development that is changing the way localisation projects are conducted. This article looks at the technology that allows for the sharing of TM databases over the internet to find out how it shapes the translator’s working environment. It shows that so-called pre-translation—until now the standard way for clients to manage translation tasks with freelancers—is giving way to web-interactive translation. Thus, rather than interacting with their own desktop databases as before, translators now interface with each other through server-based translation memories, so that a newly entered term or segment can be retrieved moments later by another translator working at a remote site. The study finds that, while the interests of most stakeholders in the localisation process are well served by this web-based arrangement, it can involve drawbacks for freelancers. Once an added value, technical expertise becomes less of a determining factor in employability, while translators lose autonomy through an inability to retain the linguistic assets they generate. Web-based TM is, therefore, seen to risk disempowering and de-skilling freelancers, relegating them from valued localisation partners to mere servants of the new technology.     

Trans Type: Development-Evaluation Cycles to Boost Translator's Productivity

We present TransType: a new approach to Machine-Aided Translation in which the human translator maintains control of the translation process while being helped by real-time completions proposed by a statistical translation engine. The TransType approach is first presented through a series of prototypes that illustrate their underlying translation model and graphical interface. The results of two rounds of in situ evaluation of TransType prototypes are discussed followed by a set of lessons learned in these experiments. It will be shown that this approach is valued by translators but given the short time allotted for the evaluation, translators were not able to quantitatively increase their productivity. TransType is compared with other approaches and new perspectives are elaborated for a new version being developed in the context of a Fifth Framework European Community Project. This revised version was published online in November 2006 with corrections to the Cover Date.     

Translating by post-editing: is it the way forward?

Translation memory tools now offer the translator to insert post-edited machine translation segments for which no match is found in the databases. The Google Translator Toolkit does this by default, advising in its Settings window: “Most users should not modify this”. Post-editing of no matches appears to work on engines trained with specific bilingual data on a source written under controlled language constraints. Would this, however, work for any type of task as Google’s advice implies? We have tested this by carrying out experiments with English–Chinese trainees, using the Toolkit to translate from the source text (the control group) and by post-editing (the experimental group). Results show that post-editing gains in productivity are marginal. With regard to quality, however, post-editing produces significantly better statistical results compared to translating manually. These gains in quality are observed independently of language direction, text difficulty or translator’s level of performance. In light of these findings, we discuss whether translators should consider post-editing as a viable alternative to conventional translation.     

利用句法信息改进交互式机器翻译

在很多领域中,全自动机器翻译的译文质量还无法达到令人满意的程度。要想获得正确无误的译文,往往需要翻译人员对自动翻译系统的输出进行后处理。在交互式机器翻译的框架内,翻译系统和译员协同工作,译员确认系统提供的译文中的最长正确前缀,系统据此对译文后缀进行预测,共同完成翻译任务。该文利用基于短语的翻译模型,建立了交互式机器翻译系统,并结合交互式机器翻译的特点,利用句法层面的子树信息来指导翻译假设的扩展。实验表明,该方法可以有效地减少人机交互次数。     

A system for terminology extraction and translation equivalent detection in real time

In this paper we present a system for automatic terminology extraction and automatic detection of the equivalent terms in the target language to be used alongside a computer assisted translation (CAT) tool that provides term candidates and their translations in an automatic way each time the translator goes from one segment to the next one. The system uses several sources of information: the text from the segment being translated and from the whole translation project, the translation memories assigned to the project and a translation phrase table from a statistical machine translation system. It also uses the terminological database assigned to the project in order to avoid presenting already known terms. The use of translation phrase tables allows us to use very large parallel corpora in a very efficient way. We have used Moses to calculate and to consult the translation phrase tables. The program is written in Python and it can be used with any CAT tool. In our experiments we have used OmegaT, a well-known open source CAT tool. Evaluation results for English–Spanish and for three subjects (politics, finance, and medicine) are presented.     

基于双语短语约束的交互式机器翻译方法

交互式机器翻译(Interactive Machine Translation,IMT)是一种通过机器翻译系统与译员之间的相互作用指导计算机解码并改善输出译文质量的技术。目前主流的IMT方法使用译员确定的前缀作为唯一约束指导解码,交互方式受限,交互效率低。该文从交互方式和解码算法两个方面对IMT方法进行改进。在交互方式方面,允许译员译前从短语译项列表中为源语言短语选择正确译项。该文还提出了基于短语表的多样性排序算法,来提高短语候选译项的多样性,并根据译员的翻译认知过程设计交互界面,改善译员在翻译过程中的用户体验。在解码算法方面,将双语短语与前缀一同作为约束参与指导解码过程,提高翻译假设评价和过滤的准确性。在LDC汉英平行语料上进行了人工评测,实验结果表明该方法较传统的IMT方法能够减轻译员的认知负担,减少翻译时间,提升翻译效率。     

UOF-OOXML文档格式转换的若干关键问题研究

主要探讨文档格式转换中的关键问题及其解决途径。文档格式转换是解决当前多种文档格式并存,互操作性差的一种有效途径。然而,现有的文档格式转换器的转换能力尚不理想,难以满足用户需求。基于UOF-OOXML格式转换器的研制情况,试图通过对当前格式转换中存在的问题的讨论,找出对格式转换影响最大的几个关键问题,并对产生这些问题的原因进行深入分析和探讨,提出相应的解决方法。该项研究对于各类文档转换器设计和提高文档互操作性均有一定的借鉴意义。     

‘Irrefragable answers’ using comparable corpora to retrieve translation equivalents

In this paper we present a tool that uses comparable corpora to find appropriate translation equivalents for expressions that are considered by translators as difficult. For a phrase in the source language the tool identifies a range of possible expressions used in similar contexts in target language corpora and presents them to the translator as a list of suggestions. In the paper we discuss the method and present results of human evaluation of the performance of the tool, which highlight its usefulness when dictionary solutions are lacking.     

Translators and TM: An investigation of translators’ perceptions of translation memory adoption

There has been little research on the role of translation memory (TM) in practitioners’ working practices, apart from reviews and a survey into ownership and rates issues. The present study provides a comprehensive snapshot of the perceptions of UK-based professional translators with regard to TM as a tool in their working environment. Moore and Benbasat’s instrument for measuring perceptions with regard to the adoption of an information technology innovation was adapted and used to investigate three hypotheses: that translators who are relatively new to the translation industry have a more positive general perception of TM than experienced translators; that translators who use TM have a more positive general perception of it than translators who do not; and, finally, that translators’ perception of the value of TM is not linked with their perceived IT proficiency. The study found that younger translators took a positive general view of TM irrespective of actual use, in particular attributing esteem to more experienced translators using (or perceived to be using) TM. Non-users at all experience levels, however, had a negative general view of TM irrespective of actual use. Both findings point to the significance of adequate knowledge in adoption decisions. Perceived IT proficiency, finally, was found to play a key role in translators’ perceptions of the benefits of TM. These findings are discussed in the light of recent trends in the translation industry, including Continuing Professional Development, quality assurance and regulation.     

Computer-assisted translation using speech recognition

Current machine translation systems are far from being perfect. However, such systems can be used in computer-assisted translation to increase the productivity of the (human) translation process. The idea is to use a text-to-text translation system to produce portions of target language text that can be accepted or amended by a human translator using text or speech. These user-validated portions are then used by the text-to-text translation system to produce further, hopefully improved suggestions. There are different alternatives of using speech in a computer-assisted translation system: From pure dictated translation to simple determination of acceptable partial translations by reading parts of the suggestions made by the system. In all the cases, information from the text to be translated can be used to constrain the speech decoding search space. While pure dictation seems to be among the most attractive settings, unfortunately perfect speech decoding does not seem possible with the current speech processing technology and human error-correcting would still be required. Therefore, approaches that allow for higher speech recognition accuracy by using increasingly constrained models in the speech recognition process are explored here. All these approaches are presented under the statistical framework. Empirical results support the potential usefulness of using speech within the computer-assisted translation paradigm.     

Target-Text Mediated Interactive Machine Translation

The use of Machine Translation as a tool for professional or other highly skilled translators is for the most part currently limited to postediting arrangements in which the translator invokes MT when desired and then manually cleans up the results. A theoretically promising but hitherto largely unsuccessful alternative to postediting for this application is interactive machine translation (IMT), in which the translator and MT system work in tandem. We argue that past failures to make IMT viable as a tool for skilled translators have been the result of an infelicitous mode of interaction rather than any inherent flaw in the idea. As a solution, we propose a new style of IMT in which the target text under construction serves as the medium of communication between an MT system and its user. We describe the design, implementation, and performance of an automatic word completion system for translators which is intended to demonstrate the feasibility of the proposed approach, albeit in a very rudimentary form.     

Automatic validation for binary translation

Binary translation is an important technique for porting programs as it allows binary code for one platform to execute on another. It is widely used in virtual machines and emulators. However, implementing a correct (and efficient) binary translator is still very challenging because many delicate details must be handled smartly. Manually identifying mistranslated instructions in an application program is difficult, especially when the application is large. Therefore, automatic validation tools are needed urgently to uncover hidden problems in a binary translator. We developed a new validation tool for binary translators. In our validation tool, the original binary code and the translated binary code run simultaneously. Both versions of the binary code continuously send their architecture states and the stored values, which are the values stored into memory cells, to a third process, the validator. Since most mistranslated instructions will result in wrong architecture states during execution, our validator can catch most mistranslated instructions emitted by a binary translator by comparing the corresponding architecture states. Corresponding architecture states may differ due to (1) translation errors, (2) different (but correct) memory layouts, and (3) return values of certain system calls. The need to differentiate the three sources of differences makes comparing architecture states very difficult, if not impossible. In our validator, we take special care to make memory layouts exactly the same and make the corresponding system calls always return exactly the same values in the original and in the translated binaries. Therefore, any differences in the corresponding architecture states indicate mistranslated instructions emitted by the binary translator. Besides solving the architecture-state-comparison problems, we also propose several methods to speed up the automatic validation. The first is the validation-block method, which reduces the number of validations while keeping the accuracy of instruction-level validation. The second is quick validation, which provides extremely fast validation at the expense of less accurate error information. Our validator can be applied to different binary translators. In our experiment, the validator has successfully validated programs translated by static, dynamic, and hybrid binary translators.     

Translation Agent: A New Metaphor for Machine Translation

We present agent metaphor as a novel interactive system to promote the efficiency in machine translation mediated communication. Machine translation is increasingly used to support multilingual communication. In the traditional, transparent-channel way of using machine translation for the multilingual communication, translation errors are ignorable, due to the quality limitation of current machine translators. Those translation errors will break the communication and lead to miscommunication. We propose to shift the paradigm from the transparent-channel metaphor to the human-interpreter metaphor, which motives the interactions between the users and the machine translator. Following this paradigm shifting, the interpreter (agent) encourages the dialog participants to collaborate, as their interactivity will be helpful in reducing the number of translation errors, the noise of the channel. We examine the translation issues raised by multilingual communication, and analyze the impact of interactivity on the elimination of translation errors. We propose an implementation of the agent metaphor, which promotes interactivity between dialog participants and the machine translator. We design the architecture of our agent, analyze the interaction process, describe decision support and autonomous behavior, and provide an example of preparing repair strategy. We conduct an English-Chinese communication task experiment on tangram arrangement. The experiment shows that compared to the transparent-channel metaphor, our agent metaphor reduces human communication effort by 21.6%.     

基于用户行为模型的计算机辅助翻译方法

与全自动机器翻译相比,计算机辅助翻译技术更具实用性,已成为机器翻译领域的一个研究热点.传统的辅助翻译过程中,用户只能被动接受系统提供的辅助译文,并进行翻译后编辑操作.该文提出一种基于用户行为模型的辅助翻译方法,通过实时记录用户的后编辑过程,分析出用户的翻译决策,建立用户行为模型,使得翻译系统能够动态获取和共享用户的翻译...     

Fully automatic multi-language translation with a catalogue of phrases: successful employment for the Swiss avalanche bulletin

The Swiss avalanche bulletin is produced twice a day in four languages. Due to the lack of time available for manual translation, a fully automated translation system is employed, based on a catalogue of predefined phrases and predetermined rules of how these phrases can be combined to produce sentences. Because this catalogue of phrases is limited to a small sublanguage, the system is able to automatically translate such sentences from German into the target languages French, Italian and English without subsequent proofreading or correction. Having been operational for two winter seasons, we assess here the quality of the produced texts based on two different surveys where participants rated texts from real avalanche bulletins from both origins, the catalogue of phrases versus manually written and translated texts. With a mean recognition rate of 55 %, users can hardly distinguish between the two types of texts, and give very similar ratings with respect to their language quality. Overall, the output from the catalogue system can be considered virtually equivalent to a text written by avalanche forecasters and then manually translated by professional translators. Furthermore, forecasters declared that all relevant situations were captured by the system with sufficient accuracy. Forecaster’s working load did not change with the introduction of the catalogue: the extra time to find matching sentences is compensated by the fact that they no longer need to double-check manually translated texts. The reduction of daily translation costs is expected to offset the initial development costs within a few years.     

Experiences with CMS2-to-Ada translation tools

This paper describes experiences with three CMS-2 to Ada language translators. It describes the evolution of an effort whose initial goal was trial usage of a single translator, into an effort which ultimately used three translators and made some comparisons of their outputs.The following points represent key findings and conclusions from these experiences. All three of the translator tools successfully generated Ada source code. However, further evaluations would be beneficial. Translation will be useful to systems and software engineers as a part of an overall transition scheme.This paper describes experiences by personnel from the Naval Surface Warfare Center, Dahlgren Division (NSWCDD) and their support contractor CSC. The work was supported by the organizations that developed the translators: Computer Command and Control Company (CCCC), Johns Hopkins University/Applied Physics Laboratory (JHU/APL), and the Navy Command, Control, and Ocean Surveillance Center (NCCOSC) Research and Development Division (NRaD).Translator operation data were collected and reviewed to develop conclusions and recommendations for further development of CMS-2-to-Ada translation technology. Two of the translators (those from CCCC and NRaD) were installed at NSWCDD and operated by NSWCDD and CSC. The third translator was operated in its native environment at JHU/APL. All three of the translators were used to translate the same segment of operational CMS-2 code—a weapons selection function.     

The Long-Term Forecast for Weather Bulletin Translation

Machine Translation (MT) is the focus of extensive scientific investigations driven by regular evaluation campaigns, but which are mostly oriented towards a somewhat particular task: translating news articles into English. In this paper, we investigate how well current MT approaches deal with a real-world task. We have rationally reconstructed one of the only MT systems in daily use which produces high-quality translation: the Météo system. We show how a combination of a sentence-based memory approach, a phrase-based statistical engine and a neural-network rescorer can give results comparable to those of the current system. We also explore another possible prospect for MT technology: the translation of weather alerts, which are currently being translated manually by translators at the Canadian Translation Bureau.