A Transformer-Based Neural Machine Translation Model for Arabic Dialects That Utilizes Subword Units

Languages that allow free word order, such as Arabic dialects, are of significant difficulty for neural machine translation (NMT) because of many scarce words and the inefficiency of NMT systems to translate these words. Unknown Word (UNK) tokens represent the out-of-vocabulary words for the reason that NMT systems run with vocabulary that has fixed size. Scarce words are encoded completely as sequences of subword pieces employing the Word-Piece Model. This research paper introduces the first Transformer-based neural machine translation model for Arabic vernaculars that employs subword units. The proposed solution is based on the Transformer model that has been presented lately. The use of subword units and shared vocabulary within the Arabic dialect (the source language) and modern standard Arabic (the target language) enhances the behavior of the multi-head attention sublayers for the encoder by obtaining the overall dependencies between words of input sentence for Arabic vernacular. Experiments are carried out from Levantine Arabic vernacular (LEV) to modern standard Arabic (MSA) and Maghrebi Arabic vernacular (MAG) to MSA, Gulf–MSA, Nile–MSA, Iraqi Arabic (IRQ) to MSA translation tasks. Extensive experiments confirm that the suggested model adequately addresses the unknown word issue and boosts the quality of translation from Arabic vernaculars to Modern standard Arabic (MSA).

Learning new verbs with known cue words: The relative effects of noun and adverb cues

Research has shown that learning a known-and-unknown word combination leads to greater learning than learning an unknown word alone (Kasahara, 2010, 2011). These studies found that attaching a known adjective to an unknown noun can help learners remember the unknown noun. Kasahara (2015) found that a known verb can serve as an effective cue to remember an unknown noun in a known-and-unknown combination. To examine useful cues to learn unknown verbs, this study compared verb (unknown) + noun (known) combinations to verb (unknown) + adverb (known) combinations. Additionally, we explored how learners’ vocabulary size would affect the known-and-unknown two-word combination learning to deepen our understanding of the characteristics of students who benefit from combination learning. The participants in each group learned 18 two-word combinations consisting of the same unknown target verbs and different known cues (nouns or adverbs). The participants were provided with a five-minute learning phase and two immediate recall tests: a Single Word Test, to write down the L1 meanings of the targets, and a Combination Test, to write down the L1 meanings of the combinations. The same two tests were administered one week later. The results showed that known nouns were better cues for learning unknown verbs than known adverbs. It was also found that participants with a larger vocabulary size benefited more from two-word combination learning.

Investigation of Vocabulary Learning Strategies to Identify Word Meanings for Saudi EFL Students in Reading Context

This study attempted to answer the following research questions related to the various vocabulary discovery strategies which are used by Saudi undergraduate learners to find unknown word meanings, the most and the least vocabulary discovery strategies the learners used to discover unknown word meanings, the relationship between the type of Vocabulary Learning Strategies used and the scores the learners accomplished on the vocabulary test, and effectiveness of the learner control and the teacher control strategy in enhancing learners’ ability to discover meanings of unknown words. Answering these questions of the study are expected to help language instructors determine the most feasible vocabulary learning strategies to help their students improve their vocabulary and so their language competences. Through purposive sampling, a group of 50 male students participated in this descriptive and analytic type of study. A questionnaire and a vocabulary test were used to collect data. The findings of the study revealed that in understanding a reading text, EFL Saudi students tend to figure out the meanings of unknown words, mainly by guessing word-meanings through different sub-strategies. The least used was the social interaction strategy. It was also found that students’ scores on the vocabulary test significantly correlated (positively and negatively) with the type of strategy they used. This study concluded that it is vital for teachers and textbook writers to design more activities to train students on the use of effective vocabulary learning strategies, mainly guessing through socially linked contextual clues.

INFERENCING FAKE WORDS’ MEANING BY MOROCCAN EFL LEARNERS

Do Moroccan EFL learners depend on the context to infer the meaning of unknown words occurring in sentences? This study investigates the way intermediate and advanced learners infer the meaning of fake words. To this end, the subjects took a test consisting of 60 items with three multiple choices. Subjects were asked to provide appropriate, inappropriate meanings of the unknown word or none of the choices without using dictionaries. The Chi-2 tests were adopted to determine whether there is a) a statistically significant difference between the three categories and b) a statistically significant difference between intermediate and advanced learners’ inferencing results. The findings demonstrate that the context along with the lexical knowledge of the L2 learners play the most important role in understanding vocabulary.

Emerging trends: Subwords, seriously?

Subwords have become very popular, but the BERTa and ERNIEb tokenizers often produce surprising results. Byte pair encoding (BPE) trains a dictionary with a simple information theoretic criterion that sidesteps the need for special treatment of unknown words. BPE is more about training (populating a dictionary of word pieces) than inference (parsing an unknown word into word pieces). The parse at inference time can be ambiguous. Which parse should we use? For example, “electroneutral” can be parsed as electron-eu-tral or electro-neutral, and “bidirectional” can be parsed as bid-ire-ction-al and bi-directional. BERT and ERNIE tend to favor the parse with more word pieces. We propose minimizing the number of word pieces. To justify our proposal, a number of criteria will be considered: sound, meaning, etc. The prefix, bi-, has the desired vowel (unlike bid) and the desired meaning (bi is Latin for two, unlike bid, which is Germanic for offer).

English Myanmar Dictionary System by Using Linear and Binary Search Techniques

This paper is aimed to develop a searching method based on binary search and linear search as well as to understand the finding of search methods. The system searches the desired word for English to English and English to Myanmar. The system may help the English may help the English Language user enable to know the desired word of English and Myanmar meaning. The system output is searching word of English meaning, Myanmar meaning, part of speech, searching time and step. And also, the system finds cross reference and user's unknown word by using binary search and linear search of searching algorithm. This system is implemented by using ASP.NET platform.