Knowledge-based Translation Research Articles

Machine translation in hindsight

The paper expands on the analysis of key projects adoring the machine translation (MT) hall of fame and their role in addressing practical tasks. The most successful initiatives suggest that the fledgling MT was contingent on the level of entropy, a.k.a. random nature of natural languages: the lower the indicator, the higher the predictability of the text, and by implication the efficiency of the system. It accounts for the success of the first Georgetown-IBM experiment and Canada’s METEO-1. The letter grew into a full-fledged system that for almost a quarter of the 20th century, provided English-French-English translations of weather bulletins, boasting high language predictability. Although, in between them the 1964 ALPAC report sowed a seed of doubt of the MT validity, it never aimed at killing the research area at all. On the contrary, it highlighted technics and applications, where the technology had demonstrated promising results, including raw MT, post-edited MT, and M-AT. The authors note a cyclic nature of the development of MT-powered methods and technologies. Today’s combination of resources and the way they are used are different very little from those employed in the past century. What makes them stand apart is the maturity of MT technologies, which made it through rulebased, direct, corpus-based, and knowledge-based translation to SMT and eventually to NMT. It has been established that the improved performance comes at a cost of more elaborate and larger data sets, tagged, marked up and annotated for automated use in language models. Taking advantage of these as well as artificial intelligence (AI), the authors venture into modeling basic text processing scenarios in a bilingual environment. This results in recommendations as to future paths for the improvement of MT technologies in the hands of professional translators by fine-tuning language models individually and pursuing post-editing (PEMT) and pre-editing practices paving the way for more complex transformations and lower equivalence levels.

Networked Artificial Intelligence English Translation System Based on an Intelligent Knowledge Base and Translation Method Thereof

Language translation is often conducted in work and study. Traditional language translation is based on lexical structure analysis. However, natural language is not so standardized, which causes this translation method to have fundamental defects, no matter how much the algorithm is improved. The translation results and human translation will be very different. This paper mainly studies the networked artificial intelligence. The English translation system and translation methods are based on a smart knowledge base. Bringing an example of English-Chinese translation to suggest translations according to the intelligent knowledge base explains in detail the principle of intelligent knowledge-based translation and the advantage of this translation method compared with the traditional translation method based on lexical structure analysis. In the experiment of this paper, when the variance is 2/N, 30/N, 100/N, and 2N, it is the experimental data for an in-depth study. When the variance is 2/N, 30/N, and 100/N, the result is the same as that when the variance is 0.5; the result when the variance is 2N also conforms to the trend in the tables, which is close to the effect of the smoothing algorithm, which verifies the effectiveness of the system in this paper.

Knowledge-Based Translation of Natural Language into Symbolic Form

Knowledge-Based Translation of Natural Language into Symbolic Form

Machine translation: a hybrid view

MT works, in the sense that everyday MT systems produce fully automatic translations that many people use with apparent benefits. The failure of intellectual breakthroughs to produce high quality, fully automatic MT is also apparent. The author considers statistical and knowledge-based translation.

Acquisition of large lexicons for practical knowledge-based MT

Although knowledge-based MT systems have the potential to achieve high translation accuracy, each successful application system requires a large amount of hand-coded lexical knowledge. Systems like KBMT-89 and its descendents have demonstrated how knowledge-based translation can produce good results in technical domains with tractable domain semantics. Nevertheless, the magnitude of the development task for large-scale applications with tens of thousands of domain concepts precludes a purely hand-crafted approach. The current challenge for the “next generation” of knowledge-based MT systems is to utilize on-line textual resources and corpus analysis software in order to automate the most laborious aspects of the knowledge acquisition process. This partial automation can in turn maximize the productivity of human knowledge engineers and help to make large-scale applications of knowledge-based MT an viable approach. In this paper we discuss the corpus-based knowledge acquisition methodology used in KANT, a knowledge-based translation system for multilingual document production. This methodology can be generalized beyond the KANT interlingua approach for use with any system that requires similar kinds of knowledge.

Computer-aided software engineering of expert systems

Abstract The problem of automatically reprogramming an expert system either to adjust for solving different types of computational problems or to improve run-time performance is addressed. A computer-aided software engineering (CASE) method using artificial intelligence techniques to instantiate a concrete program from a given abstract expert system architecture is proposed. Our method, called expert system reification, is suitable for use in a large range of problem-solving behaviors. Reification combines meta-level programming with partial evaluation of the program. Expert system reification extends the above blend with a knowledge-based translation module, thus obtaining an efficient and portable expert system version. We demonstrate the method by applying it to STAREX, an electronic circuit pack troubleshooting expert system developed in PROLOG to derive automatically the corresponding C code, which is currently installed at one of the AT&T manufacturing facilities.

Program translation via abstraction and reimplementation

An abstraction-and-reimplementation paradigm is presented in which the source program is first analyzed in order to obtain a programming-language-independent abstract understanding of the computation performed by the program as a whole. The program is then reimplemented in the target language based on this understanding. The key to this approach is the abstract understanding obtained. It allows the translator to benefit from an appreciation of the global features of the source program without being distracted by what are considered irrelevant details. Knowledge-based translation via abstraction and reimplementation is described as one of the goals of the Programmer's Apprentice project. A translator which translates Cobol programs into Hibol (a very-high-level business data processing language) has been constructed. A computer which generates extremely efficient PDP-11 object code for Pascal programs has been designed. >