Negative Transfer Effects Research Articles

Investigating Language Transfer Effect of Arabic and Spanish on English Using Test of English as Foreign Language Corpus

The first language, or also known as a mother tongue, can influence second language acquisition (SLA). Contrastive analysis theory state that mother tongue can either facilitate or hinder second language acquisition. The similarities between first language and second language facilitate the learning process while the differences between L1 and L2 could sometimes hinder the learning process and cause a negative transfer. Thus, to comprehend the impact of L1 on L2, it is necessary to analyze the language performance of EFL learners. The objective of this study was to investigate the negative transfer effect of Arabic and Spanish as a first language (L1) on learning English as a foreign language (L2). The data was collected using test of English as foreign language corpus. The researcher used a qualitative method. The errors were collected and analyzed based on rubric. The findings showed that Arabic and Spanish had a negative transfer effect on English as a second language. Arabic language had more negative transfer effect on English than Spanish language.

CRENet: Crowd region enhancement network for multi-person 3D pose estimation

Recovering multi-person 3D poses from a single image is a challenging problem due to inherent depth ambiguities, including root-relative depth and absolute root depth. Current bottom-up methods show promising potential to mitigate absolute root depth ambiguity through explicitly aggregating global contextual cues. However, these methods treat the entire image region equally during root depth regression, ignoring the negative impact of irrelevant regions. Moreover, they learn shared features for both depths, each of which focuses on different information. This sharing mechanism may result in negative transfer, thus diminishing root depth prediction accuracy. To address these challenges, we present a novel bottom-up method, Crowd Region Enhancement Network (CRENet), incorporating a Feature Decoupling Module (FDM) and a Global Attention Module (GAM). FDM explicitly learns the discriminative feature for each depth through adaptively recalibrating its channel-wise responses and fusing multi-level features, which makes the model focus on each depth prediction separately and thus avoids the adverse effect of negative transfer. GAM highlights crowd regions while suppressing irrelevant regions using the attention mechanism and further refines the attention regions based on the confidence measure about the attention, which is beneficial to learn depth-related cues from informative crowd regions and facilitate root depth estimation. Comprehensive experiments on benchmarks MuPoTS-3D and CMU Panoptic demonstrate that our method outperforms the state-of-the-art bottom-up methods in absolute 3D pose estimation and is applicable to in-the-wild images, which also indicates that learning depth-specific features and suppressing the noise signals can significantly benefit multi-person absolute 3D pose estimation.

The Negative Transfer of Shanghai Dialect on English Vowel Phonology Acquisition of Shanghai High School Students

With the accerleration of globalisation, the importance of English as an international common language has become more and more prominent. This study investigated the negative transfer effect of Shanghai dialect on English vowel acquisition in Shanghai high school students. Through the analysis of formant data of English vowel pronunciation in 49 Shanghai high school students, it is found that there is no significant change in tongue position when students pronounce some long and short vowels, the overall tongue position is low, and the distinction between long and short vowels is not obvious. The results show that the specific vowel phonemes in Shanghai dialect have a negative transfer effect on English pronunciation, which affects the accuracy of students' English pronunciation. Based on this, this study puts forward a variety of methods to improve English pronunciation teaching, including strengthening tongue position and oral shape training, using multimedia technology to assist teaching and providing personalized tutoring to help students overcome pronunciation barriers caused by dialects.

A Review of the Influence of Wu Dialect on English Pronunciation Acquisition

The Wu dialect is widely spoken in Wu-speaking regions of China, which covers a considerable number of English learners. However, the phonetic system of the Wu dialect differs significantly from that of English, which greatly affects the English learning process of Wu-speaking learners, thus attracting numerous researchers to conduct related studies. This paper provides a comprehensive overview of research on the influence of Wu dialect on English phonological learning over the past two decades and presents a selection of twenty-seven papers to explore the theoretical framework, the research methodology, and the specific impacts encountered by English language learners who speak Wu dialect. Among them, the theoretical discussion centered on language transfer theory, Fleges Speech Learning Model and Bests Perceptual Assimilation Model; the research methodology consisted of qualitative analysis and empirical study; and the focus was placed on positive and negative transfer effects, i.e., the challenges posed by the absence or presence of phonological differences in the Wu dialect to the learners of English. The findings reveal significant segmental and suprasegmental influences in English pronunciation acquisition, with segmental challenges including differentiation of consonants and incomplete articulation of vowels, and suprasegmental challenges including stress placement, intonation patterns, and phonological rhythm. In addition, the discussion highlights the need for more empirical studies to supplement qualitative analyses, the consideration of regional variations within Wu dialect, and the exploration of teaching strategies informed by research findings. Therefore, the paper advocates for future research focusing on regional differences, deeper exploration of suprasegmental influences, integration of research into teaching practices, and diversified research methods.

Navigating the unlearning landscape: an organizational unlearning taxonomy and an outcome-centric model

Purpose The purpose of this paper is to explore the phenomenon of organizational unlearning with a focus on challenging path dependence and its implications on the organizational change field. By generating a taxonomy of unlearning definitions and examining the dimensions, actors and processes involved, the authors aim to offer a holistic understanding of organizational unlearning and its potential applications for organizations facing ambiguity and uncertainty. Design/methodology/approach This conceptual paper draws the literature on organizational unlearning to map existing definitions and categorize them into a comprehensive taxonomy to propose a model focused on the outcomes. Findings The findings highlight that organizational unlearning involves the three main organizational dimensions (micro: individuals; meso: groups; macro: organizations) and that the macro-organizational perspective represents the best fit for the concept. Furthermore, the authors’ argue that the most appropriate process for understanding the unlearning phenomenon is through challenge, as it allows the questioning of the present and facilitates critical reflection. Finally, applying organizational unlearning to path dependence concept, the authors’ posit that organizations can overcome negative transfer effects and build new awareness to reinterpret their dependencies in light of environmental changes. Originality/value This study contributes to the literature on organizational unlearning by providing a comprehensive taxonomy of definitions, clarifying the dimensions, constructs and processes involved. The integration of challenging path dependence with organizational unlearning offers a novel perspective on the potential for organizational change field. The paper’s findings have practical implications for organizations striving to survive and develop in uncertain environments.

The Influence of Second Language Learning on Language Transfer in the Process of Trilingual Education

The transfer from the second to the third language is not a simple and easy-understanding process. In exploring the process of trilingual learning, this study thoroughly examines the impact of second language learning on the acquisition of a third language, particularly the phenomenon of transfer. By talking about various aspects of language transfer, the study shows multiple factors influencing language transfer, and how these factors influence the learning processes of third languages. This passage expounds both the positive and negative transfer effects of the second language towards third language learning by using the literature analysis. It comes out of the various factors such as the interlinguistic universals, the depth and breadth of second language acquisition, and the third language learning environment which influence the third language acquisition. On this basis, the paper also explores how these findings can be applied in language teaching practice to optimize the learning strategies and teaching methods of trilingual learners. This paper comes to the conclusion of the influence of the second language on the third language acquisition in the language transfer aspect.

Investigating Syntactic Transfer from English to Korean in Neural L2 Language Models

This paper investigates how the grammatical knowledge obtained in the initial language (English) of neural language models (LMs) influences the learning of grammatical structures in their second language (Korean). To achieve this objective, we conduct the now well- established experimental procedure, including (i) pre-training transformer-based GPT-2 LMs with Korean and English datasets, (ii) further fine-tuning them with a specific set of Korean data as L1 or L2, and (iii) evaluating them with the test data of KBLiMP while analyzing their linguistic generalization in L1 or L2. We have found negative transfer effects in the comparison between English as L1 and Korean as L2. Furthermore, in the trajectory analysis, the second language-learning LM has captured linguistic features of Korean including syntax, syntax-semantics interface, and morphology during the progressive training step. Our study of second language learning in LMs contributes to predicting potential syntactic challenges arising from the interference by the L1 language during the learning of Korean as a foreign language.

The Effect of Negative Transfer on English Writing of Sudanese EFL Learners

The Effect of Negative Transfer on English Writing of Sudanese EFL Learners

The Effect of L1 Negative Transfer on EFL Saudi Students’ Use of Grammar in Writing

The present study aimed at investigating the effect of the L1 negative transfer on the writing of EFL Saudi students. It examined the common grammatical errors that Saudi students made while writing in English and analyzed the sources of their errors. The samples consisted of English essays written by 74 freshmen female students enrolled at Majmaah University in Saudi Arabia. The samples were analyzed and the errors classified according to grammatical sub-categories including tenses, singular/plural markers, prepositions, articles and pronouns. This study found that 81% of the students' errors can be accounted for in terms of L1 transfer. Grammatical errors were the most frequent ones, recording 67% of the total rate of errors. More specifically, preposition (40%) and tense (32%) errors constituted the most frequent subcategories of the grammatical errors. It was concluded that learning basic linguistic differences between Saudi students' L1 and English language is a necessary condition for helping the learners to overcome the problem of first language interference in their writing.

The Positive and Negative Transfer Effects of Native Chinese Dialects in Japanese Phonological Learning

The Positive and Negative Transfer Effects of Native Chinese Dialects in Japanese Phonological Learning

Astronaut Candidate, Candidate-Like, and Undergraduate Subjects Compared on Retention and Transfer.

INTRODUCTION: The present study examined long-term retention and transfer of knowledge and skills, as well as the effect of cognitive load on retention and transfer, using a sample of astronaut candidates and two comparison groups. The first comparison group, recruited from Johnson Space Center, was similar in age, education, and general health to the astronaut candidate group; the second comparison group included university undergraduate students.METHODS:This study employed two different tasks-a simple perceptual-motor task involving data entry and a complex memory updating task requiring both prospective and retrospective memory. Subjects completed multiple sessions involving both tasks over a 500-d period, with test sessions involving transfer and/or a cognitive load manipulation. For the perceptual-motor task, transfer involved changes to the stimuli that increased intrinsic cognitive load or changes to the required motoric procedures. For the memory updating task, extraneous cognitive load was increased by the addition of a concurrent secondary task.RESULTS:For both the perceptual-motor and memory updating tasks, astronaut candidates and candidate-like subjects performed more accurately, with greater speed, and were less impacted by increased cognitive load than undergraduate students. Despite the generally superior performance of astronaut candidates and candidate-like subjects, they were more likely to experience negative transfer on the perceptual-motor task, whereas undergraduate students demonstrated positive transfer.DISCUSSION:Candidate-like subjects provided a more accurate approximation of astronaut candidate performance than did undergraduate students, especially with regard to negative transfer effects and cognitive load.Kole JA, Barshi I, Healy AF, Schneider VI. Astronaut candidate, candidate-like, and undergraduate subjects compared on retention and transfer. Aerosp Med Hum Perform. 2023; 94(12):902-910.

Evidence for positive and negative transfer of abstract task knowledge in adults and school-aged children

Engaging cognitive control is essential to flexibly adapt to constantly changing environments. However, relatively little is known about how prior task experience impacts on the engagement of cognitive control in novel task environments. We aimed to clarify how individuals learn and transfer the engagement of cognitive control with a focus on the hierarchical and temporal aspects of task knowledge. Highlighting two distinct cognitive control processes, the engagement of cognitive control in advance (proactive control) and in response to conflicts (reactive control), we conducted six preregistered online experiments with both adults (Experiment 1, 3, and 5: N = 71, N = 108, and N = 70) and 9- to 10-year-olds (Experiment 2, 4, 6: N = 69, N = 108, and N = 70). Using two different experimental paradigms, we demonstrated that prior task experience of engaging reactive control makes adults and 9-to 10-year-olds respond in a reactive way in a subsequent similar-structured condition with different stimuli in which proactive control could have been engaged. This indicates that individuals do learn knowledge about the temporal structure of task goal activation and, on occasion, negatively transfer this knowledge. Furthermore, individuals exhibited these negative transfer effects in a similar-structured condition with different task goals and stimuli, indicating that they learn hierarchically-structured task knowledge. The collective findings suggest a new way of understanding how hierarchical and temporal task knowledge influences the engagement of cognitive control and highlight potential mechanisms underlying the near transfer effects observed in cognitive control training.

Predicting viral rumors and vulnerable users with graph-based neural multi-task learning for infodemic surveillance

In the age of the infodemic, it is crucial to have tools for effectively monitoring the spread of rampant rumors that can quickly go viral, as well as identifying vulnerable users who may be more susceptible to spreading such misinformation. This proactive approach allows for timely preventive measures to be taken, mitigating the negative impact of false information on society. We propose a novel approach to predict viral rumors and vulnerable users using a unified graph neural network model. We pre-train network-based user embeddings and leverage a cross-attention mechanism between users and posts, together with a community-enhanced vulnerability propagation (CVP) method to improve user and propagation graph representations. Furthermore, we employ two multi-task training strategies to mitigate negative transfer effects among tasks in different settings, enhancing the overall performance of our approach. We also construct two datasets with ground-truth annotations on information virality and user vulnerability in rumor and non-rumor events, which are automatically derived from existing rumor detection datasets. Extensive evaluation results of our joint learning model confirm its superiority over strong baselines in all three tasks: rumor detection, virality prediction, and user vulnerability scoring. For instance, compared to the best baselines based on the Weibo dataset, our model makes 3.8% and 3.0% improvements on Accuracy and MacF1 for rumor detection, and reduces mean squared error (MSE) by 23.9% and 16.5% for virality prediction and user vulnerability scoring, respectively. Our findings suggest that our approach effectively captures the correlation between rumor virality and user vulnerability, leveraging this information to improve prediction performance and provide a valuable tool for infodemic surveillance.

Robust manifold discriminative distribution adaptation for transfer subspace learning

Transfer learning is an effective means by leveraging rich information of the labeled samples from the source domain to build an appropriate classifier for annotating the target domain. Existing transfer learning algorithms usually try to minimize the distribution difference between different domains by means of Maximum Mean Discrepancy (MMD) based metric criteria. However, the domain adaptation methods based on MMD usually have the following two problems: 1) the local geometry information of the inter-domain data is ignored, which may cause to negative transfer effect. 2) The classifier and the feature learning are not integrated into one framework, in which the global optimal solution may be not obtained. Therefore, a Robust Manifold Discriminative Distribution Adaptation (RMDDA) model is presented for transfer subspace learning. Because the difference between domains leads to the large deviation of the original features of data in the source and target domains, it is difficult to construct the manifold structure of inter-domain data. A new feature termed Histogram Feature of Neighbors (HFON) is firstly introduced to rightly capture the domain locality. Secondly, the marginal distribution, conditional distribution, and discriminative regression are integrated into a model, which can solve the problem of separating feature learning from classifier training. It is considered that the dimension of feature extraction is limited by the number of classes which is determined by discriminative regression, we introduce double projection matrices into the model. Lastly, the numerous experiments on six benchmark cross-domain image datasets prove the superiority of RMDDA algorithm compared with some state-of-the-art algorithms.

The Negative Transfer Effect on the Neural Machine Translation of Egyptian Arabic Adjuncts into English: The Case of Google Translate

Parallel corpora for low-resource Arabic dialects and English are limited and small-scale, and most neural machine translation models, including Google Translate, rely mainly on parallel corpora of standard Arabic and English to train for dialectal Arabic translation. A model well trained to translate to and from standard Arabic is believed to efficiently translate dialectal Arabic, given their similarities. This study demonstrates the impact of not using large-scale, dialect-specific parallel corpora by quantitatively and qualitatively analyzing the performance of Google Translate in translating Egyptian Arabic adjuncts. Compared to human reference translation, Google Translate achieved a low BLEU score of 14.69. Qualitative analysis showed that reliance on standard Arabic parallel corpora caused a negative transfer problem manifested in the literal translation of idiomatic adjuncts, the misinterpretation of dialectal adjuncts as main clause constituents, the translation of dialectal adjuncts after orthographically similar standard Arabic words, and the use of standard Arabic common lexical meanings to translate dialect-specific adjuncts. This study’s findings will be relevant for researchers interested in dialectal Arabic neural machine translation and has implications for investment in the development of large-scale, dialect-specific corpora to better process the peculiarities of Arabic dialects and reduce the effect of negative transfer from standard Arabic.

Cross-Domain Sentiment Analysis Based on Feature Projection and Multi-Source Attention in IoT.

Social media is a real-time social sensor to sense and collect diverse information, which can be combined with sentiment analysis to help IoT sensors provide user-demanded favorable data in smart systems. In the case of insufficient data labels, cross-domain sentiment analysis aims to transfer knowledge from the source domain with rich labels to the target domain that lacks labels. Most domain adaptation sentiment analysis methods achieve transfer learning by reducing the domain differences between the source and target domains, but little attention is paid to the negative transfer problem caused by invalid source domains. To address these problems, this paper proposes a cross-domain sentiment analysis method based on feature projection and multi-source attention (FPMA), which not only alleviates the effect of negative transfer through a multi-source selection strategy but also improves the classification performance in terms of feature representation. Specifically, two feature extractors and a domain discriminator are employed to extract shared and private features through adversarial training. The extracted features are optimized by orthogonal projection to help train the classification in multi-source domains. Finally, each text in the target domain is fed into the trained module. The sentiment tendency is predicted in the weighted form of the attention mechanism based on the classification results from the multi-source domains. The experimental results on two commonly used datasets showed that FPMA outperformed baseline models.

Asymmetric negative transfer effects of working memory training.

Gathercole et al. (Journal of Memory and Language, 105, 19-42, 2019) presented a cognitive routine framework for explaining the underlying mechanisms of working memory (WM) training and transfer. This framework conceptualizes training-induced changes as the acquisition of novel cognitive routines similar to learning a new skill. We further infer that WM training might not always generate positive outcomes because previously acquired routines may affect subsequent task performance in various ways. Thus, the present study aimed to demonstrate the negative effects of WM training via two experiments. We conducted Experiment 1online using a two-phase training paradigm with only three training sessions per phase and replicated the key findings of Gathercole and Norris (in prep.) that training on a backward circle span task (a spatial task) transferred negatively to subsequent training on a backward letter span task (a verbal task). We conducted Experiment 2using a reversed task order design corresponding to Experiment 1. The results indicated that the transfer from backward letter training to backward circle training was not negative, but rather weakly positive, suggesting that the direction of the negative transfer effect is asymmetric. The present study therefore found that a negative transfer effect can indeed occur under certain WM training designs. The presence of this asymmetric effect indicates that backward circle and backward letter tasks require different optimal routines and that the locus of negative transfer might be the acquisition process of such optimal routines. Hence, the routines already established for backward circle might hinder the development of optimal routines for backward letter, but not vice versa.

Multi-Class Transfer Learning and Domain Selection for Cross-Subject EEG Classification

Transfer learning (TL) has been proven to be one of the most significant techniques for cross-subject classification in electroencephalogram (EEG)-based brain-computer interfaces (BCI). Hence, it is widely used to address the challenges of cross-session and cross-subject variability with more accurate intention prediction. In this case, TL utilizes knowledge (signal features) in the source domain(s) to improve the classification in the target domain. However, current existing transfer learning approaches on EEG-based BCI are mostly limited to two-class cross-subject classification problems, while multi-class problems are only implemented with a focus on within-subject classification due to the complexity of multi-class cross-subject classification problems. In this paper, we first extended the transfer learning approaches to a multi-class cross-subject scenario, then investigated the reason for transfer learning performance being poor in multi-class cross-subject classification. Secondly, we address the challenge of significant sessional and subject-to-subject variations originating from both known and unknown factors. It is discovered that such variations have a massive influence on the classification because of the negative transfer (NT) across domains. Based on this discovery, we propose a multi-class transfer learning approach based on multi-source manifold feature transfer learning (MMFT) framework and an enhanced version to minimize the effects of NT. The proposed multi-class transfer learning approach extends the existing MMFT to multi-class cases. Then enhanced multi-class MMFT firstly searches for domains with high transferability and selects only the best combination among source domains (SD), then utilize the best-selected combination of domains for transfer learning. Experimental results illustrate that the proposed multi-class MMFT can be employed in the cross-subject classification of both three-class and four-class problems. Experimental results also demonstrated that the enhanced multi-class MMFT could effectively minimize the effect of negative transfer and significantly increase the prediction rates across individual target domains (TD). The highest classification accuracy (CA) of 98% is obtained by the enhanced multi-class MMFT.

Instance Weighting-Based Partial Domain Adaptation for Intelligent Fault Diagnosis of Rotating Machinery

The cross-domain fault diagnosis problem based on deep domain adaptation (deep DA) has gained great attention in recent years. However, a required but not easily satisfied assumption in many researches that the label space of the source domain and the target domain should be identical limits their applications in practice. In industrial reality, the label space of the target domain may be only a subset of that of the source domain, which is defined as partial DA diagnosis scenario. Focusing on this scenario, this paper proposes a novel diagnosis method named instance weighted mean maximum discrepancy (IWMMD). A new weighting mechanism inspired by instance discrimination is designed to realize DA on shared label space between domains. Also, discrimination structure enhancement for both domains is introduced to encourage better classification ability and safer domain alignment. The effectiveness of IWMMD is verified by two datasets. In the gear dataset, the diagnosis accuracy is 89.65% with a 5.11% improvement. In the bearing dataset, the diagnosis accuracy is 96.28% with a 4.46% improvement. The results and analysis show that the proposed method can reduce the negative transfer effects caused by outlier class samples in source domain and learn a more separable discrimination structure, which is effective in both no-partial and partial diagnosis scenarios and time-efficient.

Partial Domain Adaptation for Scene Classification From Remote Sensing Imagery

Although domain adaptation approaches have been proposed to tackle cross-regional, multitemporal, and multisensor remote sensing applications since they do not require any human interpretation in the target domain, most current works assume identical label space across the source and the target domains. However, in real-world applications, we often transfer knowledge from a large-scale dataset with rich annotations to a small-scale target dataset with scarcity of labels. In most cases, the label space of the source domain is usually large enough to subsume that of the target domain, which is termed partial domain adaptation. In this article, we propose a new partial domain adaptation algorithm for remote sensing scene classification and our proposed method contains three major parts. First, we employ a progressive auxiliary domain module to alleviate the negative transfer effect caused by outlier classes. Second, we adopt an improved domain adversarial neural network (DANN) with multiweights to better encourage domain confusion. Last but not least, we design an attentive complement entropy regularization to improve the prediction confidence for samples and avoid untransferable samples (such as the samples belonging to outlier classes in the source domain) being mistakenly classified. We collect three common remote sensing datasets to evaluate our proposed method. Our method achieves an average accuracy of 79.36%, which considerably outperforms other state-of-the-art partial domain adaptation methods with an average accuracy improvement of 1.90%–12.45% and attaining a 13.67% gain compared to the straightforward deep learning model (ResNet-50). The experiment results indicate that our approach shows promising prospects for solving more general and practical domain adaptation problems where the label space of the source domain subsumes that of the target domain.