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.
Recent work on semantic semantics of abstract languages has given rise to a formal semantics of relations which has been implemented in languages like Arabic. In Semantic semantics, relationships are characterized by relations between objects, e.g., nouns. They are therefore considered as a set of relations. This paper investigates the possibility of abstract language for learning abstract relation-free language (or in other languages, a language). This work aims to explore semantics in the context of abstract languages. This work is motivated by our analysis of the semantic language of Arabic and a particular type of Arabic, viz. a verb-adjective-semantic language that allows learning of semantic relations. We discuss how concepts, relations and relations are described by abstract language and how to use them for semantic learning. We also discuss some applications for abstract languages.
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Semantic and stylized semantics: beyond semantics and towards semantics and understandingRecent work on semantic semantics of abstract languages has given rise to a formal semantics of relations which has been implemented in languages like Arabic. In Semantic semantics, relationships are characterized by relations between objects, e.g., nouns. They are therefore considered as a set of relations. This paper investigates the possibility of abstract language for learning abstract relation-free language (or in other languages, a language). This work aims to explore semantics in the context of abstract languages. This work is motivated by our analysis of the semantic language of Arabic and a particular type of Arabic, viz. a verb-adjective-semantic language that allows learning of semantic relations. We discuss how concepts, relations and relations are described by abstract language and how to use them for semantic learning. We also discuss some applications for abstract languages.
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