Recognizing emotions in conversations is a challenging task due to the presence of contextual dependencies governed by self- and inter-personal influences. Recent approaches have focused on modeling these primarily via supervised learning. However, purely strategies demand large amounts annotated data, which lacking most available corpora this task. To tackle challenge, we look at transfer lear...

Since the quality of statistical machine translation (SMT) is heavily dependent upon the size and quality of training data, many approaches have been proposed for automatically mining bilingual text from comparable corpora. However, the existing solutions are restricted to extract either bilingual sentences or sub-sentential fragments. Instead, we present an efficient framework to extract both ...

Lately, the problem of code-switching has gained a lot of attention and has emerged as an active area of research. In bilingual communities, the speakers commonly embed the words and phrases of a non-native language into the syntax of a native language in their day-to-day communications. The code-switching is a global phenomenon among multilingual communities, still very limited acoustic and li...

Inter-sentential dependencies such as discourse connectives or pronouns have an impact on the translation of these items. These dependencies have classically been analyzed within complex theoretical frameworks, often monolingual ones, and the resulting fine-grained descriptions, although relevant to translation, are likely beyond reach of statistical machine translation systems. Instead, we pro...

This study aims to determine the relationships of Code-Switching Based Instruction on learners involvement and efficacy in mathematics. The aimed answer following questions: 1) What is extent use code-switching based instruction regarding tag-switching efficiency, inter-sentential switching, intra-sentential switching? 2) terms attention interest? 3) confidence, experience, enthusiasm? 4) Does ...