Terence Odlin reconsiders a question that many language teachers and educational researchers have addressed: how much influence can a learner's native language have in making the acquisition of a new language easy or difficult? Transfer has long been a controversial issue, but many recent studies support the view that cross-linguistic influences can have an important impact on second language acquisition. Odlin analyzes and interprets research showing many ways in which similarities and differences between languages can influence the acquisition of grammar, vocabulary, and pronunciation. In addition he provides a detailed look at work on other areas important for the study of transfer including discourse, individual variation, and sociolinguistic factors. Language teachers, applied linguists, and educational researchers will find this volume highly accessible and extremely valuable to their work.
When learners of a new language draw on their native language (or on any other that they may know), this earlier acquired linguistic knowledge may influence their success. Such cross-linguistic influence, also known as language transfer, has long raised questions about what linguists can predict about success in the new language and about what processes are involved in using prior knowledge. This book lucidly brings together many insights on transfer: e.g. on the relation between translation and transfer, the relation between comprehension and production, and the problem of how complete any predictions of difficulty may ever be. The discussions also explore implications for future research and for classroom practice. The book will thus serve as a reliable guide for teachers, researchers, translators, interpreters, and students curious about language contact.
The study of native language influence in Second Language Acquisition has undergone significant changes over the past few decades. This book, which includes 12 chapters by distinguished researchers in the field of second language acquisition, traces the conceptual history of language transfer from its early role within a Contrastive Analysis framework to its current position within Universal Grammar. The introduction presents a continuum of thought starting from the late 70s, a time in which major rethinking in the field regarding the concept of language transfer was beginning to take place, and continuing through the present day in which language transfer is integrated within current concepts and theoretical models. The afterword unites the issues discussed and allows the reader to place these issues in the context of future research. For the present book, the 1983 edition has been thoroughly revised, and some papers have been replaced and added.
Provides a comprehensive overview of third language acquisition (additive multilingualism) in adulthood, an increasingly important subfield of language acquisition.
This book explains the detectionbased approach to investigating crosslinguistic influence and illustrates the value of the approach through a collection of five empirical studies that use the approach to quantify, evaluate, and isolate the subtle and complex influences of learners’ nativelanguage backgrounds on their English writing.
The study of native language influence in Second Language Acquisition has undergone significant changes over the past few decades. This book, which includes 12 chapters by distinguished researchers in the field of second language acquisition, traces the conceptual history of language transfer from its early role within a Contrastive Analysis framework to its current position within Universal Grammar. The introduction presents a continuum of thought starting from the late 70s, a time in which major rethinking in the field regarding the concept of language transfer was beginning to take place, and continuing through the present day in which language transfer is integrated within current concepts and theoretical models. The afterword unites the issues discussed and allows the reader to place these issues in the context of future research. For the present book, the 1983 edition has been thoroughly revised, and some papers have been replaced and added.
In the context of increasingly multilingual global educational settings, this book provides a timely exploration of the phenomenon of cross-linguistic transfer of writing strategies (in particular, transfer from the foreign language to the first language) and presents a compelling case for a multilingual approach to writing pedagogy. The book presents evidence from a classroom-based intervention study conducted in a secondary school in England on cross-linguistic strategy transfer. It suggests that even beginner or low proficiency foreign language learners can develop effective skills and strategies in the foreign language classroom which can also positively influence writing in other languages, including their first language. This book ultimately encourages more joined-up, cross-curricular, cross-linguistic thinking related to language in schools by exploring the potential for collaboration between languages teachers.
When people attempt to learn a new language, the language(s) they already know can help but also hinder their understanding or production of new forms. This phenomenon, known as language transfer, is the focus of this book. The collection offers new theoretical perspectives, some in the empirical studies and some in other chapters, and consists of four sections considering lexical, syntactic, phonological and cognitive perspectives. The volume provides a wealth of studies on the influence of Chinese on the acquisition of English but also includes studies involving Finnish, French, Hindi, Korean, Persian, Spanish, Swedish and Tamil. It will be of great interest to researchers and students working in the areas of crosslinguistic influence in second language acquisition, language pedagogy and psycholinguistics.
This volume, dedicated to language transfer, starts out with state-of-the-art psycholinguistic approaches to language transfer involving studies on psycho-typological transfer, lexical interference and foreign accent. The next chapter on Transfer in Language Learning, Contact, and Change presents new empirical data from several languages (English, German, Russian, French, Italian) on various transfer phenomena ranging from second language acquisition and contact-induced change in word order to cross-linguistic influences in word formation and the lexicon. Transfer in Applied Linguistics scrutinizes, on the one hand, the external sources of language transfer by investigating bilingual resources and the school context, but also by pointing out the differences in academic language in multilingual adolescents. On the other hand, internal sources of language transfer in multilingual classrooms are illuminated. A final chapter directs its focus on methodological issues that arise when more than one language is studied systematically and it offers a solution on causal effects for the investigation of heritage language proficiencies. The chapter also includes studies that exploit more innovative methodologies on L1 identification and clitic acquisition.
Build custom NLP models in record time by adapting pre-trained machine learning models to solve specialized problems. Summary In Transfer Learning for Natural Language Processing you will learn: Fine tuning pretrained models with new domain data Picking the right model to reduce resource usage Transfer learning for neural network architectures Generating text with generative pretrained transformers Cross-lingual transfer learning with BERT Foundations for exploring NLP academic literature Training deep learning NLP models from scratch is costly, time-consuming, and requires massive amounts of data. In Transfer Learning for Natural Language Processing, DARPA researcher Paul Azunre reveals cutting-edge transfer learning techniques that apply customizable pretrained models to your own NLP architectures. You’ll learn how to use transfer learning to deliver state-of-the-art results for language comprehension, even when working with limited label data. Best of all, you’ll save on training time and computational costs. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Build custom NLP models in record time, even with limited datasets! Transfer learning is a machine learning technique for adapting pretrained machine learning models to solve specialized problems. This powerful approach has revolutionized natural language processing, driving improvements in machine translation, business analytics, and natural language generation. About the book Transfer Learning for Natural Language Processing teaches you to create powerful NLP solutions quickly by building on existing pretrained models. This instantly useful book provides crystal-clear explanations of the concepts you need to grok transfer learning along with hands-on examples so you can practice your new skills immediately. As you go, you’ll apply state-of-the-art transfer learning methods to create a spam email classifier, a fact checker, and more real-world applications. What's inside Fine tuning pretrained models with new domain data Picking the right model to reduce resource use Transfer learning for neural network architectures Generating text with pretrained transformers About the reader For machine learning engineers and data scientists with some experience in NLP. About the author Paul Azunre holds a PhD in Computer Science from MIT and has served as a Principal Investigator on several DARPA research programs. Table of Contents PART 1 INTRODUCTION AND OVERVIEW 1 What is transfer learning? 2 Getting started with baselines: Data preprocessing 3 Getting started with baselines: Benchmarking and optimization PART 2 SHALLOW TRANSFER LEARNING AND DEEP TRANSFER LEARNING WITH RECURRENT NEURAL NETWORKS (RNNS) 4 Shallow transfer learning for NLP 5 Preprocessing data for recurrent neural network deep transfer learning experiments 6 Deep transfer learning for NLP with recurrent neural networks PART 3 DEEP TRANSFER LEARNING WITH TRANSFORMERS AND ADAPTATION STRATEGIES 7 Deep transfer learning for NLP with the transformer and GPT 8 Deep transfer learning for NLP with BERT and multilingual BERT 9 ULMFiT and knowledge distillation adaptation strategies 10 ALBERT, adapters, and multitask adaptation strategies 11 Conclusions
