English machine translation is a natural language processing research direction that has important scientific research value and practical value in the current artificial intelligence boom. The variability of language, the limited ability to express semantic information, and the lack of parallel corpus resources all limit the usefulness and popularity of English machine translation in practical applications. The self-attention mechanism has received a lot of attention in English machine translation tasks because of its highly parallelizable computing ability, which reduces the model’s training time and allows it to capture the semantic relevance of all words in the context. The efficiency of the self-attention mechanism, however, differs from that of recurrent neural networks because it ignores the position and structure information between context words. The English machine translation model based on the self-attention mechanism uses sine and cosine position coding to represent the absolute position information of words in order to enable the model to use position information between words. This method, on the other hand, can reflect relative distance but does not provide directionality. As a result, a new model of English machine translation is proposed, which is based on the logarithmic position representation method and the self-attention mechanism. This model retains the distance and directional information between words, as well as the efficiency of the self-attention mechanism. Experiments show that the nonstrict phrase extraction method can effectively extract phrase translation pairs from the n-best word alignment results and that the extraction constraint strategy can improve translation quality even further. Nonstrict phrase extraction methods and n-best alignment results can significantly improve the quality of translation translations when compared to traditional phrase extraction methods based on single alignment.
NLP-based techniques can support in improving understanding of legal text documents. In this work we present a semi-automatic framework to extract signal phrases from legislative texts for an arbitrary European language. Through a case study using Dutch legislation, we demonstrate that it is feasible to extract these phrases reliably with a small number of supporting domain experts. Finally, we argue how in future works our framework could be utilized with existing methods to be applied to different languages.
The University of Texas at El Paso has launched the TeachTech Program to help its instructors to learn and implement the applications of new instructional technologies in the university classrooms. The objectives of this chapter are to examine what faculty members have experienced after taking part in the TeachTech Program. This study employed an online interview method to solicit past and present TeachTech Program participants (N=17) to share their experiences. Participants responded to a questionnaire hosted at QuestionPro. Faculty recurrent keywords and key phrases were collected from participants' experiential narratives. Using the key phrase extraction functions from QDA Miner and WordStat has found the following phrases related to their experiences: “incorporate technology,” “collaborate sessions,” “hybrid version,” “desire to learn,” and “solve problems.” Implications and discussions were provided.