This paper explores the development of an intelligent translation system for spoken English using Recurrent Neural Network (RNN) models. The fundamental principles of RNNs and their advantages in processing sequential data, particularly in handling time-dependent natural language data, are discussed. The methodology for constructing the…

This study examines students' perceptions of the Guided Use of Machine Translation (GUMT) model and their perceptions of GUMT's impact on their foreign language (FL) writing. Adapted from O'Neill (2016, 2019b), GUMT model activities were developed and implemented in an upper-elementary Korean as a FL course at a large southwestern U.S. university.…

Many studies that have investigated the educational value of online machine translation (MT) in second language (L2) writing generally report significant improvements after MT use, but no study as of yet has comprehensively analyzed the effectiveness of MT use in terms of various measures in syntactic complexity, accuracy, lexical complexity, and…

This paper explores a general approach to paraphrase generation using a pre-trained seq2seq model fine-tuned using a back-translated anatomy and physiology textbook. Human ratings indicate that the paraphrase model generally preserved meaning and grammaticality/fluency: 70% of meaning ratings were above 75, and 40% of paraphrases were considered…