1,644 research outputs found

    A Simple and Effective Method of Cross-Lingual Plagiarism Detection

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    We present a simple cross-lingual plagiarism detection method applicable to a large number of languages. The presented approach leverages open multilingual thesauri for candidate retrieval task and pre-trained multilingual BERT-based language models for detailed analysis. The method does not rely on machine translation and word sense disambiguation when in use, and therefore is suitable for a large number of languages, including under-resourced languages. The effectiveness of the proposed approach is demonstrated for several existing and new benchmarks, achieving state-of-the-art results for French, Russian, and Armenian languages

    Language-as-skill Approach in Foreign Language Education: A Phenomenological Study

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    The purpose of this qualitative phenomenological study was to understand foreign language educators\u27 lived experience of language-as-skill that focuses on language use. The central research question explored the foreign language educators\u27 experiences and perspectives on the concept of language acquisition as a type of skill acquisition. In addition, the researcher investigated foreign language educators\u27 language-as-knowledge and language-as-skill methodologies. This study also aimed to discover how the language-as-skill with advanced technology could be a way to address the contemporary challenges in foreign language education for learners and improve learners\u27 communicative competence to thrive in a globalized world with diversity. A transcendental phenomenological study design was selected to explicate the essence of human understanding. At this stage in the research, skill acquisition views Language learning as other cognitive skills development, such as how people learn to play the piano or drive a car. The theory guiding this study was DeKeyser\u27s skill acquisition theory, which explained the relationship between skill development and Language acquisition. In this study, 10 foreign language teachers from a local language training school became participants in semi-structured interviews, classroom observations, and document analysis. Data that were collected from the interviews, documentation, and observations were reviewed, grouped, coded, and reported as faithfully as possible to the participants\u27 experiences and perceptions of this phenomenological study

    Less is More: Restricted Representations for Better Interpretability and Generalizability

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    Deep neural networks are prevalent in supervised learning for large amounts of tasks such as image classification, machine translation and even scientific discovery. Their success is often at the sacrifice of interpretability and generalizability. The increasing complexity of models and involvement of the pre-training process make the inexplicability more imminent. The outstanding performance when labeled data are abundant while prone to overfit when labeled data are limited demonstrates the difficulty of deep neural networks' generalizability to different datasets. This thesis aims to improve interpretability and generalizability by restricting representations. We choose to approach interpretability by focusing on attribution analysis to understand which features contribute to prediction on BERT, and to approach generalizability by focusing on effective methods in a low-data regime. We consider two strategies of restricting representations: (1) adding bottleneck, and (2) introducing compression. Given input x, suppose we want to learn y with the latent representation z (i.e. x→z→y), adding bottleneck means adding function R such that L(R(z)) < L(z) and introducing compression means adding function R so that L(R(y)) < L(y) where L refers to the number of bits. In other words, the restriction is added either in the middle of the pipeline or at the end of it. We first introduce how adding information bottleneck can help attribution analysis and apply it to investigate BERT's behavior on text classification in Chapter 3. We then extend this attribution method to analyze passage reranking in Chapter 4, where we conduct a detailed analysis to understand cross-layer and cross-passage behavior. Adding bottleneck can not only provide insight to understand deep neural networks but can also be used to increase generalizability. In Chapter 5, we demonstrate the equivalence between adding bottleneck and doing neural compression. We then leverage this finding with a framework called Non-Parametric learning by Compression with Latent Variables (NPC-LV), and show how optimizing neural compressors can be used in the non-parametric image classification with few labeled data. To further investigate how compression alone helps non-parametric learning without latent variables (NPC), we carry out experiments with a universal compressor gzip on text classification in Chapter 6. In Chapter 7, we elucidate methods of adopting the perspective of doing compression but without the actual process of compression using T5. Using experimental results in passage reranking, we show that our method is highly effective in a low-data regime when only one thousand query-passage pairs are available. In addition to the weakly supervised scenario, we also extend our method to large language models like GPT under almost no supervision --- in one-shot and zero-shot settings. The experiments show that without extra parameters or in-context learning, GPT can be used for semantic similarity, text classification, and text ranking and outperform strong baselines, which is presented in Chapter 8. The thesis proposes to tackle two big challenges in machine learning --- "interpretability" and "generalizability" through restricting representation. We provide both theoretical derivation and empirical results to show the effectiveness of using information-theoretic approaches. We not only design new algorithms but also provide numerous insights on why and how "compression" is so important in understanding deep neural networks and improving generalizability

    An investigation into the rationale and treatment impact of removable and fixed appliances in adults: A qualitative study

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    Introduction: There is limited evidence available that focuses on the experiences of adult patients during orthodontic treatment. A better understanding of the adult patient's rationale and preferences will allow orthodontists to provide more relevant information to patients and likely to facilitate the development of a patient-centred approach to providing better care. The aim of the current study was to understand why adult patients, undergo orthodontic treatment, in particular their reasoning and overall experience with their choice of appliance. Materials: A qualitative study was conducted on adult participants recruited from four different London-based orthodontic private practises. Participants wearing fixed ceramic labial appliances (FC), removable aligner appliances (RA), and fixed lingual appliances (FL) were invited to take part in one-to-one, semi-structured interviews. Qualitative data were collected using a topic guide, until saturation was reached. Interviews were audio-recorded and transcribed verbatim and analysed using framework methodology. Results: In total, 22 participants (13 females; FC = 8, RA = 8 and FL=6), were interviewed. The data was presented under three objectives, with 2 themes for each objective and 15 overall sub-themes developed. Objective one, the reasons that lead adults to seek orthodontic treatment: theme A: psychosocial influence; theme B: health related issues. Objective two, the rationale for selecting specific treatment options: theme C: social influence and theme D: appliance features and finally, objective three, the impact of different orthodontic appliances on the quality of life of participants: theme E: functional impairment and F: psychosocial impact. Conclusions: The present research identified a number of factors influence adults in their decision-making process and treatment experiences. It is important for both orthodontists and patients to understand these findings. It is particularly important to facilitate the development of a patient-centred approach to providing better care

    Evaluating automated and hybrid neural disambiguation for African historical named entities

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    Documents detailing South African history contain ambiguous names. Ambiguous names may be due to people having the same name or the same person being referred to by multiple different names. Thus when searching for or attempting to extract information about a particular person, the name used may affect the results. This problem may be alleviated by using a Named Entity Disambiguation (NED) system to disambiguate names by linking them to a knowledge base. In recent years, transformer-based language models have led to improvements in NED systems. Furthermore, multilingual language models have shown the ability to learn concepts across languages, reducing the amount of training data required in low-resource languages. Thus a multilingual language model-based NED system was developed to disambiguate people's names within a historical South African context using documents written in English and isiZulu from the 500 Year Archive (FHYA). The multilingual language model-based system substantially improved on a probability-based baseline and achieved a micro F1-score of 0.726. At the same time, the entity linking component was able to link 81.9% of the mentions to the correct entity. However, the system's performance on documents written in isiZulu was significantly lower than on the documents written in English. Thus the system was augmented with handcrafted rules to improve its performance. The addition of handcrafted rules resulted in a small but significant improvement in performance when compared to the unaugmented NED system

    Paraphrasing evades detectors of AI-generated text, but retrieval is an effective defense

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    The rise in malicious usage of large language models, such as fake content creation and academic plagiarism, has motivated the development of approaches that identify AI-generated text, including those based on watermarking or outlier detection. However, the robustness of these detection algorithms to paraphrases of AI-generated text remains unclear. To stress test these detectors, we build a 11B parameter paraphrase generation model (DIPPER) that can paraphrase paragraphs, condition on surrounding context, and control lexical diversity and content reordering. Using DIPPER to paraphrase text generated by three large language models (including GPT3.5-davinci-003) successfully evades several detectors, including watermarking, GPTZero, DetectGPT, and OpenAI's text classifier. For example, DIPPER drops detection accuracy of DetectGPT from 70.3% to 4.6% (at a constant false positive rate of 1%), without appreciably modifying the input semantics. To increase the robustness of AI-generated text detection to paraphrase attacks, we introduce a simple defense that relies on retrieving semantically-similar generations and must be maintained by a language model API provider. Given a candidate text, our algorithm searches a database of sequences previously generated by the API, looking for sequences that match the candidate text within a certain threshold. We empirically verify our defense using a database of 15M generations from a fine-tuned T5-XXL model and find that it can detect 80% to 97% of paraphrased generations across different settings while only classifying 1% of human-written sequences as AI-generated. We open-source our models, code and data.Comment: NeurIPS 2023 camera ready (32 pages). Code, models, data available in https://github.com/martiansideofthemoon/ai-detection-paraphrase

    THE EFFECT OF TECHING TECHNIQUES (ROLE-PLAY- REPETITION) AND SPEAKING ANXIETY ON STUDENTS’ SPEAKING ABILITY AT MA NURUL ISLAM KAMPUNG BARU

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    ABSTRACT Rulia Septami, (2023): The Effect of Teaching Techniques (Role-PlayRepetition) and Speaking Anxiety on Students’ Speaking Ability at MA Nurul Islam Kampung Baru. The objective of this research were to find out whether there is significant difference in speaking ability taught by role-play and taught by repetition, to find out whether any significant difference in speaking ability between the high level anxiety students taught by using role-play and taught by repetition, to find out whether any significant difference in speaking ability between the low level anxiety students taught by role play and taught by repetition and to find out whether any significant interactional effect between teaching techniques and speaking anxiety on students’ speaking ability. This research was experimental research in the form of factorial design 2x2 post-test only. The population of the research was the eleventh grade at MA Nurul Islam Kampung Baru consisting of three classes (XI IPA 1, XI IPA2, XI IPS). In order to take the sample of the research cluster sampling technique was used. It has been selected 34 students which consist of two classes, XI IPA 1and XI IPA 2 as the sample of the research. In collecting the data speaking test was conducted in both classes after giving treatments in six meeting. The data were analyzed by using by parametric test since the assumption of normally distributed. However, the data interpreted by using Two-ways anova. Finally, the results of that analyzed were, there was a significant difference in speaking score taught by role-play and taught by repetition, There was a significant difference in speaking ability between the high level anxiety students taught by role play and taught by repetition, There was significant difference in speaking ability between the low level anxiety students taught by role play and taught by repetition, and There was no significant interactional effect between teaching techniques and speaking anxiety on speaking ability. Key words: Role-play technique, Repetition technique, Speaking Anxiety, Speaking Abilit

    A machine learning approach for Urdu text sentiment analysis

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    Product evaluations, ratings, and other sorts of online expressions have risen in popularity as a result of the emergence of social networking sites and blogs. Sentiment analysis has emerged as a new area of study for computational linguists as a result of this rapidly expanding data set. From around a decade ago, this has been a topic of discussion for English speakers. However, the scientific community completely ignores other important languages, such as Urdu. Morphologically, Urdu is one of the most complex languages in the world. For this reason, a variety of unique characteristics, such as the language's unusual morphology and unrestricted word order, make the Urdu language processing a difficult challenge to solve. This research provides a new framework for the categorization of Urdu language sentiments. The main contributions of the research are to show how important this multidimensional research problem is as well as its technical parts, such as the parsing algorithm, corpus, lexicon, etc. A new approach for Urdu text sentiment analysis including data gathering, pre-processing, feature extraction, feature vector formation, and finally, sentiment classification has been designed to deal with Urdu language sentiments. The result and discussion section provides a comprehensive comparison of the proposed work with the standard baseline method in terms of precision, recall, f-measure, and accuracy of three different types of datasets. In the overall comparison of the models, the proposed work shows an encouraging achievement in terms of accuracy and other metrics. Last but not least, this section also provides the featured trend and possible direction of the current work

    Recommendations to Solve the Problem of a Lack of Computer Skills Among CLB 4 and 5 Learners at the LINC Center

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    The purpose of this study was to provide recommendations to solve the problem of a lack of computer skills among Canadian Language Benchmark (CLB) 4 and 5 learners at the Language Instruction for Newcomers to Canada (LINC) Center. The problem was that CLB 4 and 5 learners do not have basic computer skills. When the school switched to online mode during the pandemic, most teachers were not sure how to teach and assess learners using technology. Around 75% of learners asked the coordinator to withdraw from the program as they felt they did not get the same teaching quality as the traditional method. The rationale for this study was that learning with technology may enhance the learners’ academic achievements and equip them with all the necessary skills needed in the workplace so the community would have well-trained immigrants who attract more businesses, and employers would consider the graduates of this school for employment. Consequently, the provincial government would notice a decrease in social assistance applications, and schools would get more funds. The school may also earn higher online rankings and reviews. For this reason, the central research question was, “How can the problem of a lack of computer skills among CLB 4 and 5 learners be solved at the LINC Center?” Three forms of data were collected. The first data collection method was interviews with teachers and administrators at LINC in Mississauga, Ontario. The second form of data collection was a focus group with teachers, and the third was a survey administered to all instructors. Recommendations to solve the problem included creating professional learning communities (PLCs) and providing blended professional development to teachers
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