We will see more and more applications of AI on the mobile devices. This special issue of AI Magazine is devoted to some exemplary works of AI on mobile devices. We include four works that range from mobile activity recognition and air-quality detection to machine translation and image compression. These works were chosen from a variety of sources, including the International Joint Conference on Artificial Intelligence 2011 Special Track on Integrated and Embedded AI Systems, held in Barcelona, Spain, in July 2011.

In this article, we investigate the possibility of cross-language communication using a synergy of words and pictures on mobile devices. 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. This interface creates a visual process of communication that occurs on two heterogeneous channels that can support each other. We implemented this user interface as application on the Apple iPad tablet, and performed a set of experiments to determine its usefulness as a translation aid for travellers.

In this paper, we present a language-independent method for the automatic, unsupervised extraction of non-content words from a corpus of documents. This method permits the creation of word lists that may be used in place of traditional function word lists in various natural language processing tasks. As an example we generated lists of words from a corpus of English, Chinese, and Russian posts extracted from Wikipedia articles and Wikipedia Wikitalk discussion pages. We applied these lists to the task of authorship attribution on this corpus to compare the effectiveness of lists of words extracted with this method to expert-created function word lists and frequent word lists (a common alternative to function word lists). hLDA lists perform comparably to frequent word lists. The trials also show that corpus-derived lists tend to perform better than more generic lists, and both sets of generated lists significantly outperformed the expert lists. Additionally, we evaluated the performance of an English expert list on machine translations of our Chinese and Russian documents, showing that our method also outperforms this alternative.

In 2007 we presented a paper that described the application of Natural Language Processing (NLP) and Machine Translation (MT) for the automated translation of process build instructions from English to other languages to support Ford's assembly plants in non-English speaking countries. 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 paper 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.

In 2007 we presented a paper that described the application of Natural Language Processing (NLP) and Machine Translation (MT) for the automated translation of process build instructions from English to other languages to support Ford’s assembly plants in non-English speaking countries. 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 paper 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.

We describe an approach for multi-engine machine translation that uses machine learning methods to train one or several classifiers for a given set of candidate translations. Contrary to existing approaches in quality estimation which only consider a single translation at a time, we explicitly model pairwise comparison with our feature vectors. We discuss several challenges our method is facing and discuss how lifelong machine learning could be applied to resolve these. We also show how the proposed architecture can be extended to allow human feedback to be included into the training process, improving the system's selection process over time.

Although, Chinese and Spanish are two of the most spoken languages in the world, not much research has been done in machine translation for this language pair. This paper focuses on investigating the state-of-the-art of Chinese-to-Spanish statistical machine translation (Smt), which nowadays is one of the most popular approaches to machine translation. For this purpose, we report details of the available parallel corpus which are Basic Traveller Expressions Corpus (Btec), Holy Bible and United Nations (Un). Additionally, we conduct experimental work with the largest of these three corpora to explore alternative Smt strategies by means of using a pivot language. Three alternatives are considered for pivoting: cascading, pseudo-corpus and triangulation. As pivot language, we use either English, Arabic or French. Results show that, for a phrase-based Smt system, English is the best pivot language between Chinese and Spanish. We propose a system output combination using the pivot strategies which is capable of outperforming the direct translation strategy. The main objective of this work is motivating and involving the research community to work in this important pair of languages given their demographic impact.

The performance of a Statistical Machine Translation System (SMT) system is proportionally directed to the quality and length of the parallel corpus it uses. However for some pair of languages there is a considerable lack of them. The long term goal is to construct a Japanese-Spanish parallel corpus to be used for SMT, whereas, there are a lack of useful Japanese-Spanish parallel Corpus. To address this problem, In this study we proposed a method for extracting Japanese-Spanish Parallel Sentences from Wikipedia using POS tagging and Rule-Based approach. The main focus of this approach is the syntactic features of both languages. Human evaluation was performed over a sample and shows promising results, in comparison with the baseline.

In 2007 we presented a paper that described the application of Natural Language Processing (NLP) and Machine Translation (MT) for the automated translation of process build instructions from English to other languages to support Ford’s assembly plants in non-English speaking countries. 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 paper 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.

We believe that the Doodling game benefits from a familiar metaphor, Crowdsourcing has been shown to be an effective paradigm real-time human social interactions, the potential for both for solving problems that are computationally players to rapidly improve their language skills, and above hard and for those requiring extensive data creation and all, the possibility of being fun. Because there are many labeling (Callison-Burch and Dredze 2010, Ambati and language pairs for which there is no strong financial incentive Vogel 2010, Zaidan and Callison-Burch 2011). Such a for developing language technologies, we believe engaging model holds key advantages for language data generation the online population in volume and on a volunteer over the traditional approaches (i.e., using existing parallel basis may be the only way to gather the necessary data. We corpora or a small set of expert translators) because of the note that the Duolingo system (Duolingo 2011) is in a promise of attracting a wide online audience, with immense similar vein, as it is also designed towards collecting parallel demographic diversity in terms of languages and language data from the crowd.