This article describes in detail an n-gram approach to statistical machine translation. This approach consists of a log-linear combination of a translation model based on n-grams of bilingual units, which are referred to as tuples, along with four specific feature functions. Translation\ud performance, which happens to be in the state of the art, is demonstrated with Spanish-to-English and English-to-Spanish translations of the European Parliament Plenary Sessions (EPPS).Peer Reviewe

Attention based deep learning systems have been demonstrated to be the state of the art approach for aspect-level sentiment analysis, however, end-to-end deep neural networks lack flexibility as one can not easily adjust the network to fix an obvious problem, especially when more training data is not available: e.g. when it always predicts positive when seeing the word disappointed. Meanwhile, it is less stressed that attention mechanism is likely to "over-focus" on particular parts of a sentence, while ignoring positions which provide key information for judging the polarity. In this paper, we describe a simple yet effective approach to leverage lexicon information so that the model becomes more flexible and robust. We also explore the effect of regularizing attention vectors to allow the network to have a broader "focus" on different parts of the sentence. The experimental results demonstrate the effectiveness of our approach.

Most cross-lingual sentiment classification (CLSC) research so far has been performed at sentence or document level. Aspect-level CLSC, which is more appropriate for many applications, presents the additional difficulty that we consider subsentential opinionated units which have to be mapped across languages. In this paper, we extend the possible cross-lingual sentiment analysis settings to aspect-level specific use cases. We propose a method, based on constrained SMT, to transfer opinionated units across languages by preserving their boundaries. We show that cross-language sentiment classifiers built with this method achieve comparable results to monolingual ones, and we compare different cross-lingual settings.

Evaluation of machine translation output is an important but difficult task. Over the last years, a variety of automatic evaluation measures have been studied, some of them like Word Error Rate (WER), Position Independent Word Error Rate (PER) and BLEU and NIST scores have become widely used tools for comparing different systems as well as for evaluating improvements within one system. However, these measures do not give any details about the nature of translation errors. Therefore some analysis of the generated output is needed in order to identify the main problems and to focus the research efforts. On the other hand, human evaluation is a time consuming and expensive task. In this paper, we investigate methods for using of morpho-syntactic information for automatic evaluation: standard error measures WER and PER are calculated on distinct word classes and forms in order to get a better idea about the nature of translation errors and possibilities for improvements.

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