Towards a real-time CNN end-to-end translation


Towards a real-time CNN end-to-end translation – Most of the previous works on the problem of inferring the meaning of phrases in English translations have only provided simple solutions when solving a particular translation problem, or when trying to translate a certain sentence in some languages. This paper proposes a new framework for translating phrases in English translations, namely, a graph-based translation problem. To do this, we design and optimize an interactive system in order to learn the structure of the graph from the translation process and how this structure is related to the sentence. To this end, a neural network architecture which can predict the meaning of phrases in a sentence is trained. The output of our system can be used in translation systems to learn the meaning of phrases in French language. The system has been validated as having good performance when compared to an existing translation system which has only learned the meaning of phrases from the translation process. The system has been tested on five different languages: English, German, French and Arabic. We have tested both the system and the system with different results, achieving good results, and outperforming state-of-the-art systems on English, on two different Arabic languages.

This thesis explores the use of word embeddings in machine learning to help identify the user’s emotional states (e.g. excitement or sadness) from the text of text. We demonstrate that this technique provides a powerful tool for identifying the emotional state that is associated with human emotional states in both text and visual data. Moreover, we argue that it leads to a significant gap between emotion-related content and the emotional state of a human being. We show how the use of emotion-related text can aid the identification of users’ emotional states in a variety of machine learning tasks such as sentiment analysis and emotion recognition. In particular, we illustrate how text-based emotion-related feature learning with the state-of-the-art neural network improves the robustness to human emotion detection and classification, and provides a new approach for generating emotions. We provide a comprehensive review of all previous work that has used emotion-related feature learning in emotion recognition.

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Towards a real-time CNN end-to-end translation

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  • Learning from Continuous Feedback: Learning to Order for Stochastic Constraint Optimization

    Learning User Preferences for Automated Question AnsweringThis thesis explores the use of word embeddings in machine learning to help identify the user’s emotional states (e.g. excitement or sadness) from the text of text. We demonstrate that this technique provides a powerful tool for identifying the emotional state that is associated with human emotional states in both text and visual data. Moreover, we argue that it leads to a significant gap between emotion-related content and the emotional state of a human being. We show how the use of emotion-related text can aid the identification of users’ emotional states in a variety of machine learning tasks such as sentiment analysis and emotion recognition. In particular, we illustrate how text-based emotion-related feature learning with the state-of-the-art neural network improves the robustness to human emotion detection and classification, and provides a new approach for generating emotions. We provide a comprehensive review of all previous work that has used emotion-related feature learning in emotion recognition.


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