Age, Aptitude, and Autonomy: An Exploration of Self-Guided Learning and Autonomy Development in Adult Learners

Optimal language learning facilitation requires drawing on insights from many feeder disciplines. Key among them has been the field of second language acquisition. With the significant developments in technology and the urgency for English learners to compete in today’s globalized world, there has been a particular focus on more advanced learning strategies as well as on research in the area of student autonomy. The latter type of research has tapped into insights offered not just by applied linguists but also by polyglots who have achieved high levels of fluency in multiple languages. Independent language learning has drawn further attention as it has been shown to be an important factor in the experiences of learners who have acquired exceptional levels of attainment. This has necessitated a careful analysis and some revision of extant theories of language acquisition, with some promoting self-directed language learning as perhaps the most feasible method for individuals seeking optimal language development and cultural immersion conducive to deeper, expedited learning. This research paper seeks to understand traditional theories of second language acquisition as they relate to self-directed learning, and the fostering of autonomy in adult learners with limited educational background, studying in a somewhat mixed level context. The author will examine factors such as age, motivation, and aptitude, and correlate their interpretation in the literature with observations, surveys, and analyses of students in the context under study. To these she will add an emic perspective to self-directed learning, describing her own experience with three months of self-directed language learning. The goal of this multifaceted description is to shed light on methods, learning strategies, and other variables that determine levels of attainment outside conventional language learning approaches

Diagnosing Contact Processes from their Outcomes: The Importance of Life Stages

This paper addresses the questions, Do bilingually induced and shift-induced change have different outcomes? If they do, can these differences assist us in reconstructing the prehistoric past, specifically the linguistic prehistory of the (smallscale neolithic) societies of Melanesia. A key to better interpreting differences in the outputs of contact-induced change is to understand how such change in smallscale societies actually occurs. I argue that it is important to know the life-stage loci of change. I suggest that language shift has two life-stage loci, one in early childhood, where evidence of shift, if any, is restricted to specialist lexicon, and one in adulthood. Adult language shift appears to have been rare in Melanesia. I also suggest that bilingually induced change, which entails the syntactic restructuring of one’s heritage language on the model of a second language, takes place among preadolescent children–a claim which is supported by various kinds of evidence. This understanding helps us in turn to interpret the outcomes of contact-induced change and to infer prehistoric events, since adult second-language learning typically leads to simplification, whilst childhood language learning may lead to an increase in complexity

Semantic radical consistency and character transparency effects in Chinese: an ERP study

BACKGROUND: This event-related potential (ERP) study aims to investigate the representation and temporal dynamics of Chinese orthography-to-semantics mappings by simultaneously manipulating character transparency and semantic radical consistency. Character components, referred to as radicals, make up the building blocks used dur...postprin

ANNOTATED DISJUNCT FOR MACHINE TRANSLATION

Most information found in the Internet is available in English version. However, most people in the world are non-English speaker. Hence, it will be of great advantage to have reliable Machine Translation tool for those people. There are many approaches for developing Machine Translation (MT) systems, some of them are direct, rule-based/transfer, interlingua, and statistical approaches. This thesis focuses on developing an MT for less resourced languages i.e. languages that do not have available grammar formalism, parser, and corpus, such as some languages in South East Asia. The nonexistence of bilingual corpora motivates us to use direct or transfer approaches. Moreover, the unavailability of grammar formalism and parser in the target languages motivates us to develop a hybrid between direct and transfer approaches. This hybrid approach is referred as a hybrid transfer approach. This approach uses the Annotated Disjunct (ADJ) method. This method, based on Link Grammar (LG) formalism, can theoretically handle one-to-one, many-to-one, and many-to-many word(s) translations. This method consists of transfer rules module which maps source words in a source sentence (SS) into target words in correct position in a target sentence (TS). The developed transfer rules are demonstrated on English → Indonesian translation tasks. An experimental evaluation is conducted to measure the performance of the developed system over available English-Indonesian MT systems. The developed ADJ-based MT system translated simple, compound, and complex English sentences in present, present continuous, present perfect, past, past perfect, and future tenses with better precision than other systems, with the accuracy of 71.17% in Subjective Sentence Error Rate metric

Analysis and Applications of Cross-Lingual Models in Natural Language Processing

Human languages vary in terms of both typologically and data availability. A typical machine learning-based approach for natural language processing (NLP) requires training data from the language of interest. However, because machine learning-based approaches heavily rely on the amount of data available in each language, the quality of trained model languages without a large amount of data is poor. One way to overcome the lack of data in each language is to conduct cross-lingual transfer learning from resource-rich languages to resource-scarce languages. Cross-lingual word embeddings and multilingual contextualized embeddings are commonly used to conduct cross-lingual transfer learning. However, the lack of resources still makes it challenging to either evaluate or improve such models. This dissertation first proposes a graph-based method to overcome the lack of evaluation data in low-resource languages by focusing on the structure of cross-lingual word embeddings, further discussing approaches to improve cross-lingual transfer learning by using retrofitting methods and by focusing on a specific task. Finally, it provides an analysis of the effect of adding different languages when pretraining multilingual models