Apple's Monopolistic Control Over the Tech Industry

Apple, a brand known worldwide for its electronic products, has become one of the biggest companies in tech. Its international dominance within the tech industry has led its business practices to be continually scrutinized. Many deem the company’s methods of maintaining its success as monopolistic because of the unfair control that it holds over its customers and certain areas of the industry. An opposing argument can also be made with the competition that Apple faces and how it prevents the company from exhibiting monopolistic control. However, this paper argues that Apple does exhibit monopolistic behaviour in the tech industry by examining Apple’s treatment of its customers and competing brands. The documented examples within news articles and journals of Apple demonstrating its control provide a new perspective of the company that many are unaware of

Combining Translation and Contextual Learning to Support English Learners in Elementary Level

Many schools instruct English Learners (ELs) without the use of their first language (L1), especially during the beginning stages, because they believe the presence of ELs’ native language will hinder their second language acquisition. According to recent studies, students’ first language proficiency supports their second language acquisition because languages share some common linguistic components. In this action research, the researcher intends to use L1 translation and contextual learning strategies to support ELs’ vocabulary acquisition in Grades 2 and 3 science, and students will choose which strategy(ies) they prefer to use to study the new words. Students take pre-and post-tests, and assessment results are analyzed to try to identify the effects of L1 translation in relation to other learning strategies

Multiple segmentations of Thai sentences for neural machine translation

Thai is a low-resource language, so it is often the case that data is not available in sufficient quantities to train an Neural Machine Translation (NMT) model which perform to a high level of quality. In addition, the Thai script does not use white spaces to delimit the boundaries between words, which adds more complexity when building sequence to sequence models. In this work, we explore how to augment a set of English–Thai parallel data by replicating sentence-pairs with different word segmentation methods on Thai, as training data for NMT model training. Using different merge operations of Byte Pair Encoding, different segmentations of Thai sentences can be obtained. The experiments show that combining these datasets, performance is improved for NMT models trained with a dataset that has been split using a supervised splitting tool

Enriching phrase tables for statistical machine translation using mixed embeddings

The phrase table is considered to be the main bilingual resource for the phrase-based statistical machine translation (PBSMT) model. During translation, a source sentence is decomposed into several phrases. The best match of each source phrase is selected among several target-side counterparts within the phrase table, and processed by the decoder to generate a sentence-level translation. The best match is chosen according to several factors, including a set of bilingual features. PBSMT engines by default provide four probability scores in phrase tables which are considered as the main set of bilingual features. Our goal is to enrich that set of features, as a better feature set should yield better translations. We propose new scores generated by a Convolutional Neural Network (CNN) which indicate the semantic relatedness of phrase pairs. We evaluate our model in different experimental settings with different language pairs. We observe significant improvements when the proposed features are incorporated into the PBSMT pipeline