Language acquisition in vector space

Language models can produce fluent, grammatical text. Nonetheless, some maintain that language models don't really learn language and also that, even if they did, that would not be informative for the study of human learning and processing. On the other side, there have been claims that the success of LMs obviates the need for studying linguistic theory and structure. We argue that both extremes are wrong. LMs can contribute to fundamental questions about linguistic structure, language processing, and learning. They force us to rethink arguments and ways of thinking that have been foundational in linguistics. While they do not replace linguistic structure and theory, they serve as model systems and working proofs of concept for gradient, usage-based approaches to language. We offer an optimistic take on the relationship between language models and linguistics.

Recent technological developments have attracted the use of machine learning technologies and sensors in various pavement maintenance and rehabilitation studies. To avoid excessive road damages, which cause high road maintenance costs, reduced mobility, vehicle damages, and safety concerns, the periodic maintenance of roads is necessary. As part of maintenance works, road pavement conditions should be monitored continuously. This monitoring is possible using modern distress detection methods that are simple to use, comparatively cheap, less labor-intensive, faster, safer, and able to provide data on a real-time basis. This paper proposed and developed two models: computer vision and sensor-based. The computer vision model was developed using the You Only Look Once (YOLOv5) algorithm for detecting and classifying pavement distresses into nine classes. The sensor-based model combined eight Controller Area Network (CAN) bus sensors available in most new vehicles to predict pavement distress. This research employed an extreme gradient boosting model (XGBoost) to train the sensor-based model. The results showed that the model achieved 98.42% and 97.99% area under the curve (AUC) metrics for training and validation datasets, respectively. The computer vision model attained an accuracy of 81.28% and an F1-score of 76.40%, which agree with past studies. The results indicated that both computer vision and sensor-based models proved highly efficient in predicting pavement distress and can be used to complement each other. Overall, computer vision and sensor-based tools provide cheap and practical road condition monitoring compared to traditional manual instruments.

Restricted accessMoreSectionsView PDF ToolsAdd to favoritesDownload CitationsTrack Citations ShareShare onFacebookTwitterLinked InRedditEmail Cite this article Rosenfeld Ronald 2000Incorporating linguistic structure into statistical language modelsPhil. Trans. R. Soc. A.3581311–1324http://doi.org/10.1098/rsta.2000.0588SectionRestricted accessIncorporating linguistic structure into statistical language models Ronald Rosenfeld Ronald Rosenfeld School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA Google Scholar Find this author on PubMed Search for more papers by this author Ronald Rosenfeld Ronald Rosenfeld School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA Google Scholar Find this author on PubMed Search for more papers by this author Published:15 April 2000https://doi.org/10.1098/rsta.2000.0588AbstractStatistical language models estimate the distribution of natural language for the purpose of improving various language technology applications. Ironically, the most successful models of this type take little advantage of the nature of language. I review the extent to which various aspects of natural language are captured in current models. I then describe a general framework, recently developed at our laboratory, for incorporating arbitrary linguistic structure into a statistical framework, and present a methodology for eliciting linguistic features currently missing from the model. Finally, I ponder our failure heretofore to integrate linguistic theories into a statistical framework, and suggest possible reasons for it. Previous ArticleNext Article VIEW FULL TEXT DOWNLOAD PDF FiguresRelatedReferencesDetailsCited by Toral A, Pecina P, Wang L and van Genabith J (2015) Linguistically-augmented perplexity-based data selection for language models, Computer Speech & Language, 10.1016/j.csl.2014.10.002, 32:1, (11-26), Online publication date: 1-Jul-2015. Devanbu P (2015) New Initiative: The Naturalness of Software 2015 IEEE/ACM 37th IEEE International Conference on Software Engineering (ICSE), 10.1109/ICSE.2015.190, 978-1-4799-1934-5, (543-546) Wang S, Wang S, Cheng L, Greiner R and Schuurmans D (2012) EXPLOITING SYNTACTIC, SEMANTIC, AND LEXICAL REGULARITIES IN LANGUAGE MODELING VIA DIRECTED MARKOV RANDOM FIELDS, Computational Intelligence, 10.1111/j.1467-8640.2012.00436.x, 29:4, (649-679), Online publication date: 1-Nov-2013. Tan M, Zhou W, Zheng L and Wang S (2012) A Scalable Distributed Syntactic, Semantic, and Lexical Language Model, Computational Linguistics, 10.1162/COLI_a_00107, 38:3, (631-671), Online publication date: 1-Sep-2012. Lee R, Jonathan P and Ziman P (2010) Pictish symbols revealed as a written language through application of Shannon entropy, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 466:2121, (2545-2560), Online publication date: 8-Sep-2010. Lee S, Baker J, Song J and Wetherbe J (2010) An Empirical Comparison of Four Text Mining Methods 2010 43rd Hawaii International Conference on System Sciences, 10.1109/HICSS.2010.48, 978-1-4244-5509-6, (1-10) This Issue15 April 2000Volume 358Issue 1769Discussion Meeting Issue ‘Computers, language and speech: formal theories and statistical data’ organized by the Royal Society and the British Academy Article InformationDOI:https://doi.org/10.1098/rsta.2000.0588Published by:Royal SocietyPrint ISSN:1364-503XOnline ISSN:1471-2962History: Published online15/04/2000Published in print15/04/2000 License: Citations and impact KeywordsHuman language technologiesStatistical language modellingFeature induction

Accessible generative artificial intelligence (AI) tools like large-language models (LLMs) (e.g., Chat-GPT, 1 Minerva 2 ) are raising a flurry of questions about the potential and implications of generative algorithms and the ethical use of AI-generated text in a variety of contexts, including open science (Bugbee & Ramachandran, 2023), student assessment (Heidt, 2023), and medicine (Harrer, 2023) . Similarly, among the graphic and visual arts communities, the use of generative image synthesis algorithms (e.g., DALL-E, 3 Midjourney, 4 Stable Diffusion 5 ) that take text prompts as input and produce works in the style of a particular human artist, or no artist who ever lived, are causing consternation and posing challenging questions (Murphy, 2022; Plunkett, 2022) . The use of generative AI to create deep fakes has also been in the spotlight (Ruiter, 2021), as has its role in answering scientific research questions directly (Castelvecchi, 2023) . To our minds, the questions these technologies are raising do not seem to be of a fundamentally different character to questions asked about AI for many years. They largely concern (a) what is possible, (b) what is right, and (c) the implications of the technology's use. For instance,

Background: Our study aims to develop a new algorithm that utilizes computer vision to accurately assess the composition of thyroid nodules and calculate the Thyroid Imaging Reporting & Data System (TIRADS) score based on uploaded nodule images. This prospective observational study evaluates the performance of our computer vision model in correctly classifying the composition of thyroid nodules. Methods: We developed a computer vision and machine learning model using the nocode AI tool “Levity” to accurately classify the composition of thyroid nodules as “Solid”, “Cystic”, “Mixed solid-cystic”, or “Spongiform”. The model was trained on 200 thyroid nodule images, labeled by an experienced endocrinologist. We tested the model on 50 additional images using an easy-to-use chatbot tool and assessed the clinician’s agreement with the diagnosis. The tool can be accessed for public use using the following link: https://tinyurl.com/2pfehpq8. Statistical analysis using the Chi-square test, Pearson correlation coefficient and point-biserial correlation coefficient was performed using Python and R. p value of less than 0.05 was considered as statistically significant. Results: The computer model diagnosed 50 test images, with 34 solid, 5 mixed solid-cystic, 7 purely cystic, and 4 spongiform. Clinician agreement was 64% and partial agreement was 20%, resulting in 84% total agreement. Chi-squared test showed a statistically significant relationship between model diagnosis and clinician agreement (p value of 0.0000059403). Pearson’s correlation test showed a statistically significant correlation between the percentage confidence of the model and clinician agreement (p value of 0.034). The model was confident in 56% of cases, with 60.7% confidence when the clinician agreed and 57.9% for partial agreement. Using the point-biserial correlation, a statistically significant correlation was found between the percentage value given by the model and agreement of the clinician (p value of 0.00182). Using the ROC curve, an optimal cutoff of 52.28% was determined for rejecting the model diagnosis. AUC was 0.85, indicating good performance. Conclusions: In conclusion, our study successfully developed a computer vision and machine learning model using Levity to accurately classify thyroid nodule composition. Our model showed good performance with statistically significant results in clinician agreement, Chi-squared test, Pearson’s correlation test, and point-biserial correlation. The tool can be accessed for public use, and an optimal cutoff of 52.28% was determined for rejecting the model diagnosis.

In this paper we argue that investigation of third language (L3) acquisition by adults and children provides essential new insights about the language learning process that neither the study of first language (L1) nor second language (L2) acquisition alone can provide. The focus of this paper concerns the role the learner's L1 plays in succeeding language acquisition. Specifically, does the L1 maintain a privileged role in all subsequent language acquisition or is it possible that all languages known can play a role in subsequent language acquisition? Results from several ongoing L3 acquisition studies investigating adults and children learning English as an L3, whose L1 is Kazakh and whose L2 is Russian, are reported in this paper. We compare the learners' patterns of acquisition in production of three types of restrictive relative clauses. Adults are compared to children; speakers are compared at comparable levels of linguistic competence in the languages. Results indicate that the L1 does not play a privileged role in subsequent acquisition. At the same time, results indicate subtle differences between adults and children. We argue that such findings would not be possible in the context of an L1 or L2 study alone. Results are used to support the proposed Cumulative Enhancement Model for Language Acquisition.

THE last 10 years have witnessed rapid growth in the popularity of graphical models, most notably Bayesian networks, as a tool for representing, learning, and computing complex probability distributions. Graphical models provide an explicit representation of the statistical dependencies between the components of a complex probability model, effectively marrying probability theory and graph theory. As Jordan puts it in [2], graphical models are “a natural tool for dealing with two problems that occur throughout applied mathematics and engineering—uncertainty and complexity—and, in particular, they are playing an increasingly important role in the design and analysis of machine learning algorithms.” Graphical models provide powerful computational support for the Bayesian approach to computer vision, which has become a standard framework for addressing vision problems. Many familiar tools from the vision literature, such as Markov random fields, hidden Markov models, and the Kalman filter, are instances of graphical models. More importantly, the graphical models formalism makes it possible to generalize these tools and develop novel statistical representations and associated algorithms for inference and learning. The history of graphical models in computer vision follows closely that of graphical models in general. Research by Pearl [3] and Lauritzen [4] in the late 1980s played a seminal role in introducing this formalism to areas of AI and statistical learning. Not long after, the formalism spread to fields such as statistics, systems engineering, information theory, pattern recognition, and, among others, computer vision. One of the earliest occurrences of graphical models in the vision literature was a paper by Binford et al. [1]. The paper described the use of Bayesian inference in a hierarchical probability model to match 3D object models to groupings of curves in a single image. The following year marked the publication of Pearl’s influential book [3] on graphical models. Since then, many technical papers have been published in IEEE journals and conference proceedings that address different aspects and applications of graphical models in computer vision. Our goal in organizing this special section was to demonstrate the breadth of applicability of the graphical models formalism to vision problems. Our call for papers in February 2002 produced 16 submissions. After a careful review process, we selected six papers for publication, including five regular papers, and one short paper. These papers reflect the state-of-the-art in the use of graphical models in vision problems that range from low-level image understanding to high-level scene interpretation. We believe these papers will appeal both to vision researchers who are actively engaged in the use of graphical models and machine learning researchers looking for a challenging application domain. The first paper in this section is “Stereo Matching Using Belief Propagation” by J. Sun, N.-N. Zheng, and H.-Y. Shum. The authors describe a new stereo algorithm based on loopy belief propagation, a powerful inference technique for complex graphical models in which exact inference is intractable. They formulate the dense stereo matching problem as MAP estimation on coupled Markov random fields and obtain promising results on standard test data sets. One of the benefits of this formulation, as the authors demonstrate, is the ease with which it can be extended to handle multiview stereo matching. In their paper “Statistical Cue Integration of DAG Deformable Models” S.K. Goldenstein, C. Vogler, and D. Metaxas describe a scheme for combining different sources of information into estimates of the parameters of a deformable model. They use a DAG representation of the interdependencies between the nodes in a deformable model. This framework supports the efficient integration of information from edges and other cues using the machinery of affine arithmetic and the propagation of uncertainties. They present experimental results for a face tracking application. Y. Song, L. Goncalves, and P. Perona describe, in their paper “Unsupervised Learning of Human Motion,” a method for learning probabilistic models of human motion from video sequences in cluttered scenes. Two key advantages of their method are its unsupervised nature, which can mitigate the need for tedious hand labeling of data, and the utilization of graphical model constraints to reduce the search space when fitting a human figure model. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 25, NO. 7, JULY 2003 785

As artificial intelligence, particularly language models like ChatGPT, become more advanced and prevalent, it's crucial to understand how this technology is shaping the way the younger generations are learning and acquiring the English language.As the field of artificial intelligence progresses at a rapid pace, marked by significant advancements exemplified by language models such as ChatGPT, the importance of delving deeper into the repercussions of this technological innovation on the learning processes of today's youth becomes increasingly evident. Specifically, understanding how AI impacts the acquisition and development of the English language among younger generations is crucial in light of the expanding presence and complexity of tools like ChatGPT. The far-reaching implications these technologies bear on education and language acquisition cannot be overstated, underscoring the need for educators and language enthusiasts to grasp the transformative effects of AI on contemporary language learning techniques and vocabulary assimilation.The evolving technological landscape presents a dual-edged sword of challenges and opportunities for those involved in education and language studies. It is imperative to develop a holistic comprehension of how AI is reshaping language learning approaches and enriching vocabulary acquisition practices in the present era. By scrutinizing the ways in which advanced language models influence learning environments for the youth, valuable insights are gleaned into the shifting paradigms of language acquisition and proficiency enhancement. Thus, a vigilant and analytical observation of the changing role of AI, particularly through language models like ChatGPT, in fortifying the foundation of English language education for today's young learners is essential. A nuanced examination of how AI technology intersects with language acquisition processes equips us to adapt educational methodologies effectively, leveraging the advantages of these advancements while resolving any emerging hurdles. Maintaining a well-informed perspective on the influence of AI, especially language models such as ChatGPT, on the linguistic development of young students is integral to optimizing the utilization of this technology. By doing so, we enhance their English language learning journeys, ensuring that they benefit from enriched experiences and achieve superior outcomes in their linguistic proficiency.

The development of Image AI has been remarkable. In addition to Computer Vision Models (CVMs) that use supervised learning on large-scale data such as ImageNet-21 K, CVMs that use self-supervised learning and Contrastive Language Image Pre-training (CLIP) have been developed recently. We have been researching human environmental perception using photos. We measured the visual similarity by having many participants manually classify similar photos. We had three photo sets: 100 garden landscapes, 242 cityscapes, and 200 student life photos. We investigated how closely 26 types of pre-trained CVMs matched the human sense. We also used SoftMax regression to select a synthetic CVM from these CVMs that best correlated with the visual similarity. The optimal CVM combinations varied significantly across photo sets. For garden landscapes, which only have garden photos, semantic segmentation and self-supervised CVM were effective, while for student life photos, which have a wide variety of photos, supervised CVM was effective. For cityscapes, which have intermediate variations, self-supervised CVM was effective. MDS was used to examine in detail how the optimal CVM was like human perception and how it differed. For garden landscapes, humans and the CVM agreed on the judgment of garden size but differed on the judgment of garden style. In the case of cityscapes and student life photos, the recognition of patterns in the photos was roughly consistent between humans and the CVM, but the CVM made more detailed classifications. It was also suggested that some of the differences between the two stems from human representation. Although some differences remain, we found that by combining CVMs effectively, it is possible to construct a CVM that is quite close to human senses.

Learning human languages is a difficult task for a computer. However, Deep Learning (DL) techniques have enhanced performance significantly for almost all-natural language processing (NLP) tasks. Unfortunately, these models cannot be generalized for all the NLP tasks with similar performance. NLU (Natural Language Understanding) is a subset of NLP including tasks, like machine translation, dialogue-based systems, natural language inference, text entailment, sentiment analysis, etc. The advancement in the field of NLU is the collective performance enhancement in all these tasks. Even though MTL (Multi-task Learning) was introduced before Deep Learning, it has gained significant attention in the past years. This paper aims to identify, investigate, and analyze various language models used in NLU and NLP to find directions for future research. The Systematic Literature Review (SLR) is prepared using the literature search guidelines proposed by Kitchenham and Charters on various language models between 2011 and 2021. This SLR points out that the unsupervised learning method-based language models show potential performance improvement. However, they face the challenge of designing the general-purpose framework for the language model, which will improve the performance of multi-task NLU and the generalized representation of knowledge. Combining these approaches may result in a more efficient and robust multi-task NLU. This SLR proposes building steps for a conceptual framework to achieve goals of enhancing the performance of language models in the field of NLU.

The utilization of assumptions will flow two linguistic groups in language teaching based on psycholinguistic studies. Psycholinguistics is a field of study as a combination of (interdisciplinary) between psychology and linguistics. One relevant issue that we can lift from psycholinguistics in relation to the use of assumptions structural linguistics and generative transformation is the issue of language acquisition (language acquisition). There are two theories about language acquisition: code-cognitive learning theory (the cognitive-code learning theory), which is generated by the TG, and the theory of the formation of habits (habit-formation theory) of structural linguistics. In utilizing the assumptions and models of language acquisition over the teacher must make several considerations: If the language acquisition is a formation of habits (habit formation), language teachers should develop a concrete program. If it is selected, then some of the activities need to be done include: a. ask the students to imitate the models presented teachers b. given patterns of exercise (drill pattern), to declare customs behave (habit of behavior) c. give students reinforcement (reinforcement) for the response (answer) is right and reduce (eliminate, Eliminate) response is wrong, and d. let students learn inductively, interpreting the rules that have been learned. Conversely, if the acquisition of language is to learn about the code (a matter of cognitive learning code), language teachers must ensure that students are able to internalize (internalized) rules that allow the student to produce sentences. Explanation about sentence structure and knowledge of the rules should be placed in a large scale rule in language teaching

This book presents recent advances by leading researchers in computational modelling of language acquisition. Sophisticated theoretical models can now be tested using simulation techniques and large corpora of linguistic data. Renewed interest in learning neural networks and the ability to test new solutions to fundamental problems has fuelled debates in an already very active field. The twenty-four authors in this collection of new work have been drawn from departments of linguistics, cognitive science, psychology, and computer science. The book as a whole shows what light may be thrown on fundamental problems when powerful computational techniques are combined with real data A central question addressed in the book concerns the extent to which linguistic structure is readily available in the environment. The authors consider the evidence in relation to word boundaries and phonotactic structure, stress patterns, text-to-speech rules, and the mapping of lexical semantics, one author arguing that a childs own output may serve as a key source of linguistic input. Linguistic structure-environment relations are central to the debate on the degree to which language learning is inductive or deductive: this issue is considered here in studies of the acquisition of pluralization and inflectional morphology. The book examines the power and utility of different modeling formalisms for different problems and approaches: how far, for example, can connectionist models be used as models for language acquisition or Simple Recurrent Networks form the basis of a model of language acquisition? To what degree can lexical items and categories be used in the construction of neural network models, or Markov chains be deployed to investigate the characteristics of a general language learning algorithm (Triggering Learning Algorithm)? This book will appeal to linguists, psychologists, cognitive scientists working in language acquisition. It will also interest those involved in computational modelling in linguistics and behavioural science.

A variety of computer vision problems can be optimally posed as Bayesian labeling in which the solution of a problem is defined as the maximum a posteriori (MAP) probability estimate of the true labeling. The posterior probability is usually derived from a prior model and a likelihood model. The latter relates to how data is observed and is problem domain dependent. The former depends on how various prior constraints are expressed. Markov Random Field Models (MRF) theory is a tool to encode contextual constraints into the prior probability. This paper presents a unified approach for MRF modeling in low and high level computer vision. The unification is made possible due to a recent advance in MRF modeling for high level object recognition. Such unification provides a systematic approach for vision modeling based on sound mathematical principles.

Expectation-based theories of sentence processing posit that processing difficulty is determined by predictability in context. While predictability quantified via surprisal has gained empirical support, this representation-agnostic measure leaves open the question of how to best approximate the human comprehender's latent probability model. This article first describes an incremental left-corner parser that incorporates information about common linguistic abstractions such as syntactic categories, predicate-argument structure, and morphological rules as a computational-level model of sentence processing. The article then evaluates a variety of structural parsers and deep neural language models as cognitive models of sentence processing by comparing the predictive power of their surprisal estimates on self-paced reading, eye-tracking, and fMRI data collected during real-time language processing. The results show that surprisal estimates from the proposed left-corner processing model deliver comparable and often superior fits to self-paced reading and eye-tracking data when compared to those from neural language models trained on much more data. This may suggest that the strong linguistic generalizations made by the proposed processing model may help predict humanlike processing costs that manifest in latency-based measures, even when the amount of training data is limited. Additionally, experiments using Transformer-based language models sharing the same primary architecture and training data show a surprising negative correlation between parameter count and fit to self-paced reading and eye-tracking data. These findings suggest that large-scale neural language models are making weaker generalizations based on patterns of lexical items rather than stronger, more humanlike generalizations based on linguistic structure.

Our work empirically assesses a novel hypothesis that language acquisition theories can help design more effective curriculum learning strategies for Cognitively-inspired Small-Scale Language Models (SSLMs). SSLMs are Transformer-based language models trained on corpora that approximate the volume and nature of input that a first-language learner can expect to receive during language acquisition. Curriculum Learning (CL) has emerged as a promising method to improve the cognitive plausibility of SSLMs in the first BabyLM Shared Task, as a way to gradually introduce more complex linguistic phenomena into the model later in training in a manner that is similar to human language acquisition. However, CL strategies have not led to considerable improvements over non-curriculum models. This is contrary to the predictions of linguistic theory, which suggests that children naturally focus on input that is neither too simple nor too difficult but at the right level of challenge for learning. This acquisition behaviour, known as the “Goldilocks Effect”, is a form of self-selecting curriculum learning that appears to naturally occur in first language (L1) acquisition. We compare the success of three curricula (Growing, Inwards & MMM) that precisely replicate the predictions of contrastive acquisition theories based on contemporary Chomksyan acquisition models to specify fine-grained curriculum learning strategies on a volume of Child-Directed Speech (CDS) that a learner would expect to receive by 6 years-old (6;0) for five languages. Overall, Curriculum Learning strategies that precisely replicate language acquisition theories, formulated based on careful analysis of child developmental sequences, can lead to better-performing data-efficient architectures.