Improve Machine Translation Research Articles

Blitz Latin Revisited

SummaryDevelopment of the machine translator Blitz Latin between the years 2002 and 2015 is discussed. Key issues remain the ambiguity in meaning of Latin stems and inflections, and the word order of the Latin language. Attempts to improve machine translation of Latin are described by the programmer.

Materiality, New Economics and Dynamics in Controlled Cultural Writing: A Derridean Exploration

Advanced information technologies have triggered a return to materialism with a focus on rapid re/production of cultural resources in multimedia applications. Machine-enabled translation has emerged as one possible solution to achieving the goal of massive re/production and transmission of cultural materials in diverse languages for international cross-cultural communication on the web. In light of the need of improving machine translation (MT) performance, controlled cultural writing (CCW) is here proposed as an alternative to general language writing. CCW refigures the format and re-presents the utterances of cultural texts emphasizing material presence of factual information for easy machine rendition. In addition to its materialistic feature, CCW undergoes a dramatic modification through paraphrase so that its meaning or thematic message is far from matching the original one. This dynamic feature is justified by the Derridean concepts of dissemination and difference. One more feature of CCW is its function of new economics resulting from its economical way of reproducing cultural information through multilingual MT application. Drawing on the controlled writings of Chinese folk texts as a case study, this paper explores the three attributes of CCW by using Derridean deconstruction concepts as the theoretical framework.

Improving Statistical Machine Translation for a Resource-Poor Language Using Related Resource-Rich Languages

We propose a novel language-independent approach for improving machine translation for resource-poor languages by exploiting their similarity to resource-rich ones. More precisely, we improve the translation from a resource-poor source language X_1 into a resource-rich language Y given a bi-text containing a limited number of parallel sentences for X_1-Y and a larger bi-text for X_2-Y for some resource-rich language X_2 that is closely related to X_1. This is achieved by taking advantage of the opportunities that vocabulary overlap and similarities between the languages X_1 and X_2 in spelling, word order, and syntax offer: (1) we improve the word alignments for the resource-poor language, (2) we further augment it with additional translation options, and (3) we take care of potential spelling differences through appropriate transliteration. The evaluation for Indonesian- >English using Malay and for Spanish -> English using Portuguese and pretending Spanish is resource-poor shows an absolute gain of up to 1.35 and 3.37 BLEU points, respectively, which is an improvement over the best rivaling approaches, while using much less additional data. Overall, our method cuts the amount of necessary "real'' training data by a factor of 2--5.

Human interaction for high-quality machine translation

Introduction Translation from a source language into a target language has become a very important activity in recent years, both in official institutions (such as the United Nations and the EU, or in the parliaments of multilingual countries like Canada and Spain), as well as in the private sector (for example, to translate user's manuals or newspapers articles). Prestigious clients such as these cannot make do with approximate translations; for all kinds of reasons, ranging from the legal obligations to good marketing practice, they require target-language texts of the highest quality. The task of producing such high-quality translations is a demanding and time-consuming one that is generally conferred to expert human translators. The problem is that, with growing globalization, the demand for high-quality translation has been steadily increasing, to the point where there are just not enough qualified translators available today to satisfy it. This has dramatically raised the need for improved machine translation (MT) technologies. The field of MT has undergone something of a revolution over the last 15 years, with the adoption of empirical, data-driven techniques originally inspired by the success of automatic speech recognition. Given the requisite corpora, it is now possible to develop new MT systems in a fraction of the time and with much less effort than was previously required under the formerly dominant rule-based paradigm. As for the quality of the translations produced by this new generation of MT systems, there has also been considerable progress; generally speaking, however, it remains well below that of human translation. No one would seriously consider directly using the output of even the best of these systems to translate a CV or a corporate Web site, for example, without submitting the machine translation to a careful human revision. As a result, those who require publication-quality translation are forced to make a diffcult choice between systems that are fully automatic but whose output must be attentively post-edited, and computer-assisted translation systems (or CAT tools for short) that allow for high quality but to the detriment of full automation. Currently, the best known CAT tools are translation memory (TM) systems. These systems recycle sentences that have previously been translated, either within the current document or earlier in other documents. This is very useful for highly repetitive texts, but not of much help for the vast majority of texts composed of original materials. Since TM systems were first introduced, very few other types of CAT tools have been forthcoming. Notable exceptions are the TransType system and its successor TransType2 (TT2). These systems represent a novel rework-ing of the old idea of interactive machine translation (IMT). Initial efforts on TransType are described in detail in Foster; suffice it to say here the system's principal novelty lies in the fact the human-machine interaction focuses on the drafting of the target text, rather than on the disambiguation of the source text, as in all former IMT systems. In the TT2 project, this idea was further developed. A full-fledged MT engine was embedded in an interactive editing environment and used to generate suggested completions of each target sentence being translated. These completions may be accepted or amended by the translator; but once validated, they are exploited by the MT engine to produce further, hopefully improved suggestions. This is in marked contrast with traditional MT, where typically the system is first used to produce a complete draft translation of a source text, which is then post-edited (corrected) offline by a human translator. TT2's interactive approach offers a significant advantage over traditional post-editing. In the latter paradigm, there is no way for the system, which is off-line, to benefit from the user's corrections; in TransType, just the opposite is true. As soon as the user begins to revise an incorrect segment, the system immediately responds to that new information by proposing an alternative completion to the target segment, which is compatible with the prefix that the user has input. Another notable feature of the work described in this article is the importance accorded to a formal treatment of human-machine interaction, something that is seldom considered in the now-prevalent framework of statistical pattern recognition.

Improving Machine Translation Using Hybrid Dictionary-Graph Based Word Sense Disambiguation with Semantic and Statistical Methods

This paper describes a machine translation prototype in which noun phrase translation is defined as a subtask of machine translation. A dedicated noun phrase translation subsystem is built and improved to translate Arabic noun phrase into English using only minimal resources for both the source and the target language. This work proposes a dictionary-graph based WSD approach to improve machine translation using hybrid semantic-statistical method based on computing words relatedness and a statistical measure of association to get the relation between ambiguous words. This relation was used with viterbi search algorithm to find the appropriate translation of the Arabic noun phrase. A shallow source language analysis, combined with a translation dictionary and a mapping system of source language phenomena into the target language and a target language corpus for generation are all the resources needed in the described system.

Improving Machine Translation Performance by Exploiting Non-Parallel Corpora

We present a novel method for discovering parallel sentences in comparable, non-parallel corpora. We train a maximum entropy classifier that, given a pair of sentences, can reliably determine whether or not they are translations of each other. Using this approach, we extract parallel data from large Chinese, Arabic, and English non-parallel newspaper corpora. We evaluate the quality of the extracted data by showing that it improves the performance of a state-of-the-art statistical machine translation system. We also show that a good-quality MT system can be built from scratch by starting with a very small parallel corpus (100,000 words) and exploiting a large non-parallel corpus. Thus, our method can be applied with great benefit to language pairs for which only scarce resources are available.