Automatic text summarization extracts important information from texts and presents the in form of a summary. Abstractive approaches progressed significantly by switching to deep neural networks, but results are not yet satisfactory, especially for languages where large training sets do exist. In several natural language processing tasks, cross-lingual model transfer is successfully applied les...

Learning to paraphrase supports both writing ability and reading comprehension, particularly for less skilled learners. As such, educational tools that integrate automated evaluations of paraphrases can be used provide timely feedback enhance learner paraphrasing skills more efficiently effectively. Paraphrase identification is a popular NLP classification task involves establishing whether two...

Aiming at detecting pronunciation errors produced by second language learners and providing corrective feedbacks related with articulation, we address effective articulatory models based on deep neural network (DNN). Articulatory attributes are defined for manner and place of articulation. In order to efficiently train these models of non-native speech without such data, which is difficult to c...

We study the selection of transfer languages for different Natural Language Processing tasks, specifically sentiment analysis, named entity recognition and dependency parsing. In order to select an optimal language, we propose utilize linguistic similarity metrics measure distance between make choice language based on this information instead relying intuition. demonstrate that correlates with ...