Out-of-vocabulary word translation is a major problem for the translation of low-resource languages that suffer from a lack of parallel training data. This paper evaluates the contributions of target-language context models towards the translation of OOV words, specifically in those cases where OOV translations are derived from external knowledge sources, such as dictionaries. We develop both neural and non-neural context models and evaluate them within both phrase-based and self-attention based neural machine translation systems. Our results show that neural language models that integrate additional context beyond the current sentence are the most effective in disambiguating possible OOV word translations. We present an efficient second-pass lattice-rescoring method for wide-context neural language models and demonstrate performance improvements over state-of-the-art self-attention based neural MT systems in five out of six low-resource language pairs.

Craig Stern is the Marketing Director for the Americas at Systran Software Inc. a market leader in secure neural machine translation. An SDSU graduate, his feats have included: filing a patent in college, built and launched a machine translation mobile app and a social venture in the eyewear industry. He has in-depth expertise with disruptive technology. In addition, he is a prolific content writer, covering the convergence of neural machine translation, voice-to-text, economics and culture. His content has been published in Forbes, Inc., Entrepreneur and The Next Web, in addition to his articles in Disruptor Daily.

The code has been copied to your clipboard. Some machines can take something written in one language and give users the same or similar wording in another language. These machines are designed to do this kind of work quickly and without mistakes. Some of the devices are so small they can be carried around the world. The quality of translation software programs has greatly improved in recent years, thanks to new, fast-developing technologies.

To understand the potential of these new systems, it helps to know how current machine translation works. The current de facto standard is Google Translate, a system that covers 103 languages from Afrikaans to Zulu, including the top 10 languages in the world–in order, Mandarin, Spanish, English, Hindi, Bengali, Portuguese, Russian, Japanese, German, and Javanese. Google's system uses human-supervised neural networks that compare parallel texts–books and articles that have been previously translated by humans. By comparing extremely large amounts of these parallel texts, Google Translate learns the equivalences between any two given languages, thus acquiring the ability to quickly translate between them. Sometimes the translations are funny or don't really capture the original meaning but, in general, they are functional and, overtime, they're getting better and better.

Deep Learning is being aggressively used in day-to-day tasks. It especially excels in areas where there is a degree of'humanness' involved, e.g. Probably the most useful feature of Deep Networks, unlike other Machine Learning algorithms, is that their performance increases as it gets more data. So if it is possible to get more data, a performance increase can be expected. One of the tasks where deep networks excel is machine translation.

Machine translation (MT) was one of the first applications of artificial intelligence technology that was deployed to solve real-world problems. Since the early 1960s, researchers have been building and utilizing computer systems that can translate from one language to another without requiring extensive human intervention. In the late 1990s, Ford Vehicle Operations began working with Systran Software Inc. to adapt and customize its machine-translation technology in order to translate Ford's vehicle assembly build instructions from English to German, Spanish, Dutch, and Portuguese. The use of machine translation was made necessary by the vast amount of dynamic information that needed to be translated in a timely fashion. The assembly build instructions at Ford contain text written in a controlled language as well as unstructured remarks and comments.

In this article, we investigate the possibility of cross-language communication using a synergy of words and pictures on mobile devices. On the one hand, communicating with only pictures is in itself a very powerful strategy, but is limited in expressiveness. On the other hand, words can express everything you could wish to say, but they are cumbersome to work with on mobile devices and need to be translated in order for their meaning to be understood. Automatic translations can contain errors that pervert the communication process, and this may undermine the users' confidence when expressing themselves across language barriers. Our idea is to create a user interface for cross-language communication that uses pictures as the primary mode of input, and words to express the detailed meaning.

We are very pleased to republish here extended versions of a sample of the papers drawn from the Innovative Applications of Artificial Intelligence Conference (IAAI-06), which was held July 17-20, 2006, in Boston, Massachusetts. Three of these articles describe deployed applications and two describe emerging applications. By this measure, artificial intelligence is going strong. Evidence comes from the annual Conference on Innovative Applications of Artificial Intelligence (IAAI), the premier conference on AI applications. The papers presented at the conference provide compelling case studies of the value and impact of AI technology.

Machine translation of human languages (for example, Japanese, English, Spanish) was one of the earliest goals of computer science research, and it remains an elusive one. Like many AI tasks, translation requires an immense amount of knowledge about language and the world. Recent approaches to machine translation frequently make use of text-based learning algorithms to fully or partially automate the acquisition of knowledge. This article illustrates these approaches. Anyone who has taken a graduate-level course in AI knows the answer.

This project has continued to evolve with the addition of new languages and improvements to the translation process. However, we discovered that there was a large demand for automated language translation across all of Ford Motor Company and we decided to expand the scope of our project to address these requirements. This article will describe our efforts to meet all of Ford's internal translation requirements with AI and MT technology and focus on the challenges and lessons that we learned from applying advanced technology across an entire corporation. Our initial goal was to utilize MT to translate vehicle build instructions from English to the native languages in the countries and regions where our assembly plants are located. The source text utilized a controlled language that we developed, called Standard Language, and we initially thought that applying MT technology would be a straightforward process. Controlled languages, such as Standard language, restrict the complexity and ambiguity of human languages by restricting syntax and terminology (Huijsen 1998). As such, they have been utilized in a number of different industrial applications (Godden 2000). However, there were many issues dealing with technical terminology, ungrammatical aspects of Standard Language, Ford-specific terminology, and the need to process uncontrolled text that needed to be addressed. We partnered with Systran Software Incorporated and with AppTek (now SAIC) to use their machine-translation technology and also incorporated natural language processing (NLP) algorithms within our artificial intelligence environment to analyze terminology and modify the source text to improve translation accuracy (Rychtyckyj 2007). The need to support manufacturing expansion in non-English speaking countries in Eastern Europe and Asia (such as in Russian and Chinese) led us to add additional language capability and to develop translation glossaries for all of the supported languages. The automated language translation for manufacturing work continues and will expand as Ford's global manufacturing footprint increases.