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Journal articles on the topic 'Neural language models'

Author: Grafiati

Published: 25 May 2024

Last updated: 31 July 2025

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1

Buckman, Jacob, and Graham Neubig. "Neural Lattice Language Models." Transactions of the Association for Computational Linguistics 6 (December 2018): 529–41. http://dx.doi.org/10.1162/tacl_a_00036.

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In this work, we propose a new language modeling paradigm that has the ability to perform both prediction and moderation of information flow at multiple granularities: neural lattice language models. These models construct a lattice of possible paths through a sentence and marginalize across this lattice to calculate sequence probabilities or optimize parameters. This approach allows us to seamlessly incorporate linguistic intuitions — including polysemy and the existence of multiword lexical items — into our language model. Experiments on multiple language modeling tasks show that English neu

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Bengio, Yoshua. "Neural net language models." Scholarpedia 3, no. 1 (2008): 3881. http://dx.doi.org/10.4249/scholarpedia.3881.

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Dong, Li. "Learning natural language interfaces with neural models." AI Matters 7, no. 2 (2021): 14–17. http://dx.doi.org/10.1145/3478369.3478375.

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Language is the primary and most natural means of communication for humans. The learning curve of interacting with various services (e.g., digital assistants, and smart appliances) would be greatly reduced if we could talk to machines using human language. However, in most cases computers can only interpret and execute formal languages.

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De Coster, Mathieu, and Joni Dambre. "Leveraging Frozen Pretrained Written Language Models for Neural Sign Language Translation." Information 13, no. 5 (2022): 220. http://dx.doi.org/10.3390/info13050220.

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We consider neural sign language translation: machine translation from signed to written languages using encoder–decoder neural networks. Translating sign language videos to written language text is especially complex because of the difference in modality between source and target language and, consequently, the required video processing. At the same time, sign languages are low-resource languages, their datasets dwarfed by those available for written languages. Recent advances in written language processing and success stories of transfer learning raise the question of how pretrained written

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Mandy Lau. "Artificial intelligence language models and the false fantasy of participatory language policies." Working papers in Applied Linguistics and Linguistics at York 1 (September 13, 2021): 4–15. http://dx.doi.org/10.25071/2564-2855.5.

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Artificial intelligence neural language models learn from a corpus of online language data, often drawn directly from user-generated content through crowdsourcing or the gift economy, bypassing traditional keepers of language policy and planning (such as governments and institutions). Here lies the dream that the languages of the digital world can bend towards individual needs and wants, and not the traditional way around. Through the participatory language work of users, linguistic diversity, accessibility, personalization, and inclusion can be increased. However, the promise of a more partic

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Chang, Tyler A., and Benjamin K. Bergen. "Word Acquisition in Neural Language Models." Transactions of the Association for Computational Linguistics 10 (2022): 1–16. http://dx.doi.org/10.1162/tacl_a_00444.

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Abstract We investigate how neural language models acquire individual words during training, extracting learning curves and ages of acquisition for over 600 words on the MacArthur-Bates Communicative Development Inventory (Fenson et al., 2007). Drawing on studies of word acquisition in children, we evaluate multiple predictors for words’ ages of acquisition in LSTMs, BERT, and GPT-2. We find that the effects of concreteness, word length, and lexical class are pointedly different in children and language models, reinforcing the importance of interaction and sensorimotor experience in child lang

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Mezzoudj, Freha, and Abdelkader Benyettou. "An empirical study of statistical language models: n-gram language models vs. neural network language models." International Journal of Innovative Computing and Applications 9, no. 4 (2018): 189. http://dx.doi.org/10.1504/ijica.2018.095762.

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Mezzoudj, Freha, and Abdelkader Benyettou. "An empirical study of statistical language models: n-gram language models vs. neural network language models." International Journal of Innovative Computing and Applications 9, no. 4 (2018): 189. http://dx.doi.org/10.1504/ijica.2018.10016827.

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Qi, Kunxun, and Jianfeng Du. "Translation-Based Matching Adversarial Network for Cross-Lingual Natural Language Inference." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (2020): 8632–39. http://dx.doi.org/10.1609/aaai.v34i05.6387.

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Cross-lingual natural language inference is a fundamental task in cross-lingual natural language understanding, widely addressed by neural models recently. Existing neural model based methods either align sentence embeddings between source and target languages, heavily relying on annotated parallel corpora, or exploit pre-trained cross-lingual language models that are fine-tuned on a single language and hard to transfer knowledge to another language. To resolve these limitations in existing methods, this paper proposes an adversarial training framework to enhance both pre-trained models and cl

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Angius, Nicola, Pietro Perconti, Alessio Plebe, and Alessandro Acciai. "The Simulative Role of Neural Language Models in Brain Language Processing." Philosophies 9, no. 5 (2024): 137. http://dx.doi.org/10.3390/philosophies9050137.

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This paper provides an epistemological and methodological analysis of the recent practice of using neural language models to simulate brain language processing. It is argued that, on the one hand, this practice can be understood as an instance of the traditional simulative method in artificial intelligence, following a mechanistic understanding of the mind; on the other hand, that it modifies the simulative method significantly. Firstly, neural language models are introduced; a study case showing how neural language models are being applied in cognitive neuroscience for simulative purposes is

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Hale, John T., Luca Campanelli, Jixing Li, Shohini Bhattasali, Christophe Pallier, and Jonathan R. Brennan. "Neurocomputational Models of Language Processing." Annual Review of Linguistics 8, no. 1 (2022): 427–46. http://dx.doi.org/10.1146/annurev-linguistics-051421-020803.

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Efforts to understand the brain bases of language face the Mapping Problem: At what level do linguistic computations and representations connect to human neurobiology? We review one approach to this problem that relies on rigorously defined computational models to specify the links between linguistic features and neural signals. Such tools can be used to estimate linguistic predictions, model linguistic features, and specify a sequence of processing steps that may be quantitatively fit to neural signals collected while participants use language. Progress has been helped by advances in machine

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Lytvynov, A., P. Andreicheva, V. Bredikhin, and V. Verbytska. "DEVELOPMENT TENDENCIES OF GENERATION MODELS OF THE UKRAINIAN LANGUAGE." Municipal economy of cities 3, no. 184 (2024): 10–15. http://dx.doi.org/10.33042/2522-1809-2024-3-184-10-15.

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The article explores the development of language generation technologies, from machine learning models to fluid neural networks for text generation. English is one of the most widespread languages in the world: it is the official language of more than 60 countries. The events of recent years have led to the development of the popularity of the Ukrainian language not only in the country but also abroad. The article analyses scientific sources on this topic, the results of which formed a base for creating a genealogical tree of the development of language synthesis technology. The study pays par

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Klemen, Matej, and Slavko Zitnik. "Neural coreference resolution for Slovene language." Computer Science and Information Systems, no. 00 (2021): 60. http://dx.doi.org/10.2298/csis201120060k.

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Coreference resolution systems aim to recognize and cluster together mentions of the same underlying entity. While there exist large amounts of research on broadly spoken languages such as English and Chinese, research on coreference in other languages is comparably scarce. In this work we first present SentiCoref 1.0 - a coreference resolution dataset for Slovene language that is comparable to English-based corpora. Further, we conduct a series of analyses using various complex models that range from simple linear models to current state-of-the-art deep neural coreference approaches leveragin

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Kunchukuttan, Anoop, Mitesh Khapra, Gurneet Singh, and Pushpak Bhattacharyya. "Leveraging Orthographic Similarity for Multilingual Neural Transliteration." Transactions of the Association for Computational Linguistics 6 (December 2018): 303–16. http://dx.doi.org/10.1162/tacl_a_00022.

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We address the task of joint training of transliteration models for multiple language pairs ( multilingual transliteration). This is an instance of multitask learning, where individual tasks (language pairs) benefit from sharing knowledge with related tasks. We focus on transliteration involving related tasks i.e., languages sharing writing systems and phonetic properties ( orthographically similar languages). We propose a modified neural encoder-decoder model that maximizes parameter sharing across language pairs in order to effectively leverage orthographic similarity. We show that multiling

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Park, Myung-Kwan, Keonwoo Koo, Jaemin Lee, and Wonil Chung. "Investigating Syntactic Transfer from English to Korean in Neural L2 Language Models." Studies in Modern Grammar 121 (March 30, 2024): 177–201. http://dx.doi.org/10.14342/smog.2024.121.177.

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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 n

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Bayer, Ali Orkan, and Giuseppe Riccardi. "Semantic language models with deep neural networks." Computer Speech & Language 40 (November 2016): 1–22. http://dx.doi.org/10.1016/j.csl.2016.04.001.

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Chuchupal, V. Y. "Neural language models for automatic speech Recognition." Речевые технологии, no. 1-2 (2020): 27–47. http://dx.doi.org/10.58633/2305-8129_2020_1-2_27.

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Tian, Yijun, Huan Song, Zichen Wang, et al. "Graph Neural Prompting with Large Language Models." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 17 (2024): 19080–88. http://dx.doi.org/10.1609/aaai.v38i17.29875.

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Large language models (LLMs) have shown remarkable generalization capability with exceptional performance in various language modeling tasks. However, they still exhibit inherent limitations in precisely capturing and returning grounded knowledge. While existing work has explored utilizing knowledge graphs (KGs) to enhance language modeling via joint training and customized model architectures, applying this to LLMs is problematic owing to their large number of parameters and high computational cost. Therefore, how to enhance pre-trained LLMs using grounded knowledge, e.g., retrieval-augmented

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Schomacker, Thorben, and Marina Tropmann-Frick. "Language Representation Models: An Overview." Entropy 23, no. 11 (2021): 1422. http://dx.doi.org/10.3390/e23111422.

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In the last few decades, text mining has been used to extract knowledge from free texts. Applying neural networks and deep learning to natural language processing (NLP) tasks has led to many accomplishments for real-world language problems over the years. The developments of the last five years have resulted in techniques that have allowed for the practical application of transfer learning in NLP. The advances in the field have been substantial, and the milestone of outperforming human baseline performance based on the general language understanding evaluation has been achieved. This paper imp

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Takahashi, Shuntaro, and Kumiko Tanaka-Ishii. "Evaluating Computational Language Models with Scaling Properties of Natural Language." Computational Linguistics 45, no. 3 (2019): 481–513. http://dx.doi.org/10.1162/coli_a_00355.

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In this article, we evaluate computational models of natural language with respect to the universal statistical behaviors of natural language. Statistical mechanical analyses have revealed that natural language text is characterized by scaling properties, which quantify the global structure in the vocabulary population and the long memory of a text. We study whether five scaling properties (given by Zipf’s law, Heaps’ law, Ebeling’s method, Taylor’s law, and long-range correlation analysis) can serve for evaluation of computational models. Specifically, we test n-gram language models, a probab

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Oba, Miyu. "Research Background on Second Language Acquisition in Neural Language Models." Journal of Natural Language Processing 32, no. 2 (2025): 684–90. https://doi.org/10.5715/jnlp.32.684.

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Mukhamadiyev, Abdinabi, Mukhriddin Mukhiddinov, Ilyos Khujayarov, Mannon Ochilov, and Jinsoo Cho. "Development of Language Models for Continuous Uzbek Speech Recognition System." Sensors 23, no. 3 (2023): 1145. http://dx.doi.org/10.3390/s23031145.

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Automatic speech recognition systems with a large vocabulary and other natural language processing applications cannot operate without a language model. Most studies on pre-trained language models have focused on more popular languages such as English, Chinese, and various European languages, but there is no publicly available Uzbek speech dataset. Therefore, language models of low-resource languages need to be studied and created. The objective of this study is to address this limitation by developing a low-resource language model for the Uzbek language and understanding linguistic occurrence

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Martin, Andrea E. "A Compositional Neural Architecture for Language." Journal of Cognitive Neuroscience 32, no. 8 (2020): 1407–27. http://dx.doi.org/10.1162/jocn_a_01552.

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Hierarchical structure and compositionality imbue human language with unparalleled expressive power and set it apart from other perception–action systems. However, neither formal nor neurobiological models account for how these defining computational properties might arise in a physiological system. I attempt to reconcile hierarchy and compositionality with principles from cell assembly computation in neuroscience; the result is an emerging theory of how the brain could convert distributed perceptual representations into hierarchical structures across multiple timescales while representing int

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Naveenkumar, T. Rudrappa, V. Reddy Mallamma, and Hanumanthappa M. "KHiTE: Multilingual Speech Acquisition to Monolingual Text Translation." Indian Journal of Science and Technology 16, no. 21 (2023): 1572–79. https://doi.org/10.17485/IJST/v16i21.727.

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Abstract <strong>Objectives:</strong> To develop a system that accepts cross-lingual spoken reviews consisting of two to four languages, translate to target language text for Indic languages namely Kannada, Hindi, Telugu and/or English termed as cross lingual speech identification and text translation system. <strong>Methods:</strong> Hybridization of software engineering models are used in natural languages for pre-processing such as noise removal and speech splitting to obtain phonemes. Combinatorial models namely Hidden-Markov-Model, Artificial Neural Networks, Deep Neural Ne

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Penner, Regina V. "Large Language Models: А Socio-Philosophical Essay". Galactica Media: Journal of Media Studies 6, № 3 (2024): 83–100. http://dx.doi.org/10.46539/gmd.v6i3.502.

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Neural networks have filled the information space. On the one hand, this indicates the scientific and technological movement of contemporary society (perhaps, AGI is already waiting for us outside the door). On the other hand, in everyday discourse there are extensive discussions about the fact that when neural networks are created, a person is left with hard work. However, a holistic understanding of the neural network is associated with a movement from the mythotechnological framework to the phenomenon itself and the questioning of its social role. The key aim of the paper is returning, thro

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Hafeez, Rabab, Muhammad Waqas Anwar, Muhammad Hasan Jamal, et al. "Contextual Urdu Lemmatization Using Recurrent Neural Network Models." Mathematics 11, no. 2 (2023): 435. http://dx.doi.org/10.3390/math11020435.

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In the field of natural language processing, machine translation is a colossally developing research area that helps humans communicate more effectively by bridging the linguistic gap. In machine translation, normalization and morphological analyses are the first and perhaps the most important modules for information retrieval (IR). To build a morphological analyzer, or to complete the normalization process, it is important to extract the correct root out of different words. Stemming and lemmatization are techniques commonly used to find the correct root words in a language. However, a few stu

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Oralbekova, Dina, Orken Mamyrbayev, Mohamed Othman, Dinara Kassymova, and Kuralai Mukhsina. "Contemporary Approaches in Evolving Language Models." Applied Sciences 13, no. 23 (2023): 12901. http://dx.doi.org/10.3390/app132312901.

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This article provides a comprehensive survey of contemporary language modeling approaches within the realm of natural language processing (NLP) tasks. This paper conducts an analytical exploration of diverse methodologies employed in the creation of language models. This exploration encompasses the architecture, training processes, and optimization strategies inherent in these models. The detailed discussion covers various models ranging from traditional n-gram and hidden Markov models to state-of-the-art neural network approaches such as BERT, GPT, LLAMA, and Bard. This article delves into di

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Yogatama, Dani, Cyprien de Masson d’Autume, and Lingpeng Kong. "Adaptive Semiparametric Language Models." Transactions of the Association for Computational Linguistics 9 (2021): 362–73. http://dx.doi.org/10.1162/tacl_a_00371.

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Abstract We present a language model that combines a large parametric neural network (i.e., a transformer) with a non-parametric episodic memory component in an integrated architecture. Our model uses extended short-term context by caching local hidden states—similar to transformer-XL—and global long-term memory by retrieving a set of nearest neighbor tokens at each timestep. We design a gating function to adaptively combine multiple information sources to make a prediction. This mechanism allows the model to use either local context, short-term memory, or long-term memory (or any combination

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Constantinescu, Ionut, Tiago Pimentel, Ryan Cotterell, and Alex Warstadt. "Investigating Critical Period Effects in Language Acquisition through Neural Language Models." Transactions of the Association for Computational Linguistics 13 (January 24, 2024): 96–120. https://doi.org/10.1162/tacl_a_00725.

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Abstract Humans appear to have a critical period (CP) for language acquisition: Second language (L2) acquisition becomes harder after early childhood, and ceasing exposure to a first language (L1) after this period (but not before) typically does not lead to substantial loss of L1 proficiency. It is unknown whether these CP effects result from innately determined brain maturation or as a stabilization of neural connections naturally induced by experience. In this study, we use language models (LMs) to test the extent to which these phenomena are peculiar to humans, or shared by a broader class

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Tinn, Robert, Hao Cheng, Yu Gu, et al. "Fine-tuning large neural language models for biomedical natural language processing." Patterns 4, no. 4 (2023): 100729. http://dx.doi.org/10.1016/j.patter.2023.100729.

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Karyukin, Vladislav, Diana Rakhimova, Aidana Karibayeva, Aliya Turganbayeva, and Asem Turarbek. "The neural machine translation models for the low-resource Kazakh–English language pair." PeerJ Computer Science 9 (February 8, 2023): e1224. http://dx.doi.org/10.7717/peerj-cs.1224.

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The development of the machine translation field was driven by people’s need to communicate with each other globally by automatically translating words, sentences, and texts from one language into another. The neural machine translation approach has become one of the most significant in recent years. This approach requires large parallel corpora not available for low-resource languages, such as the Kazakh language, which makes it difficult to achieve the high performance of the neural machine translation models. This article explores the existing methods for dealing with low-resource languages

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Lai, Yihan. "Enhancing Linguistic Bridges: Seq2seq Models and the Future of Machine Translation." Highlights in Science, Engineering and Technology 111 (August 19, 2024): 410–14. https://doi.org/10.54097/pf2xsr76.

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Machine translation has evolved significantly since the introduction of rule-based and statistical methods, leading to groundbreaking advances with the advent of neural networks. These neural networks, particularly sequence-to-sequence (seq2seq) models, have revolutionized the field by enabling more fluent and contextually accurate translations. As digital interactions increase globally, the demand for efficient and precise translation tools has never been more pressing, especially for language pairs that pose substantial linguistic challenges due to their structural differences. This study de

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Choi, Sunjoo, Myung-Kwan Park, and Euhee Kim. "How are Korean Neural Language Models ‘surprised’ Layerwisely?" Journal of Language Sciences 28, no. 4 (2021): 301–17. http://dx.doi.org/10.14384/kals.2021.28.4.301.

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Zhang, Peng, Wenjie Hui, Benyou Wang, et al. "Complex-valued Neural Network-based Quantum Language Models." ACM Transactions on Information Systems 40, no. 4 (2022): 1–31. http://dx.doi.org/10.1145/3505138.

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Language modeling is essential in Natural Language Processing and Information Retrieval related tasks. After the statistical language models, Quantum Language Model (QLM) has been proposed to unify both single words and compound terms in the same probability space without extending term space exponentially. Although QLM achieved good performance in ad hoc retrieval, it still has two major limitations: (1) QLM cannot make use of supervised information, mainly due to the iterative and non-differentiable estimation of the density matrix, which represents both queries and documents in QLM. (2) QLM

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Lee, Jaemin, and Jeong-Ah Shin. "Evaluating L2 Training Methods in Neural Language Models." Lanaguage Research 60, no. 3 (2024): 323–45. https://doi.org/10.30961/lr.2024.60.3.323.

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Tanaka, Tomohiro, Ryo Masumura, and Takanobu Oba. "Neural candidate-aware language models for speech recognition." Computer Speech & Language 66 (March 2021): 101157. http://dx.doi.org/10.1016/j.csl.2020.101157.

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Kong, Weirui, Hyeju Jang, Giuseppe Carenini, and Thalia S. Field. "Exploring neural models for predicting dementia from language." Computer Speech & Language 68 (July 2021): 101181. http://dx.doi.org/10.1016/j.csl.2020.101181.

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Phan, Tien D., and Nur Zincir‐Heywood. "User identification via neural network based language models." International Journal of Network Management 29, no. 3 (2018): e2049. http://dx.doi.org/10.1002/nem.2049.

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Budaya, I. Gede Bintang Arya, Made Windu Antara Kesiman, and I. Made Gede Sunarya. "The Influence of Word Vectorization for Kawi Language to Indonesian Language Neural Machine Translation." Journal of Information Technology and Computer Science 7, no. 1 (2022): 81–93. http://dx.doi.org/10.25126/jitecs.202271387.

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People relatively use machine translation to learn any textual knowledge beyond their native language. There is already robust machine translation such as Google translate. However, the language list has only covered the high resource language such as English, France, etc., but not for Kawi Language as one of the local languages used in Bali's old works of literature. Therefore, it is necessary to study the development of machine translation from the Kawi language to the more active user language such as the Indonesian language to make easier learning access for the young learner. The research

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Studenikina, Kseniia Andreevna. "Evaluation of neural models’ linguistic competence: evidence from Russian predicate agreement." Proceedings of the Institute for System Programming of the RAS 34, no. 6 (2022): 178–84. http://dx.doi.org/10.15514/ispras-2022-34(6)-14.

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This study investigates the linguistic competence of modern language models. Artificial neural networks demonstrate high quality in many natural language processing tasks. However, their implicit grammar knowledge remains unstudied. The ability to judge a sentence as grammatical or ungrammatical is regarded as key property of human’s linguistic competence. We suppose that language models’ grammar knowledge also occurs in their ability to judge the grammaticality of a sentence. In order to test neural networks’ linguistic competence, we probe their acquisition of number predicate agreement in R

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Meijer, Erik. "Virtual Machinations: Using Large Language Models as Neural Computers." Queue 22, no. 3 (2024): 25–52. http://dx.doi.org/10.1145/3676287.

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We explore how Large Language Models (LLMs) can function not just as databases, but as dynamic, end-user programmable neural computers. The native programming language for this neural computer is a Logic Programming-inspired declarative language that formalizes and externalizes the chain-of-thought reasoning as it might happen inside a large language model.

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Rane, Kirti, Tanaya Bagwe,, Shruti Chaudhari, Ankita Kale, and Gayatri Deore. "Enhancing En-X Translation: A Chrome Extension-Based Approach to Indic Language Models." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem42782.

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Language translation is the lifeblood of any communication that crosses linguistic boundaries. Recent trends in the domain of neural machine translation (NMT) are already superior to the old traditions. In such circumstances, the works done by Prahwini et al. (2024) and Vandan Mujadia et al. (2024) highlight the application of NMT for resource-constrained Indian languages. In view of many challenges like parallel corpus scarcity, we present a real-time adaptable translation model that works on the Fairseq framework. It provides high-accuracy translations for Assamese, Gujarati, Kannada, Bengal

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Goldberg, Yoav. "A Primer on Neural Network Models for Natural Language Processing." Journal of Artificial Intelligence Research 57 (November 20, 2016): 345–420. http://dx.doi.org/10.1613/jair.4992.

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Over the past few years, neural networks have re-emerged as powerful machine-learning models, yielding state-of-the-art results in fields such as image recognition and speech processing. More recently, neural network models started to be applied also to textual natural language signals, again with very promising results. This tutorial surveys neural network models from the perspective of natural language processing research, in an attempt to bring natural-language researchers up to speed with the neural techniques. The tutorial covers input encoding for natural language tasks, feed-forward net

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Zhao, Xiaodong, Rouyi Fan, and Wanyue Liu. "Research on Transformer-Based Multilingual Machine Translation Methods." Journal of Intelligence and Knowledge Engineering 3, no. 1 (2025): 57–67. https://doi.org/10.62517/jike.202504108.

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Because of the great difference in word order between different languages in machine translation, the translation model has the problem of wrong translation. Translation models with the same target language and different source languages learn different word order information, resulting in different translation quality. Therefore, this paper proposes a multilingual neural machine translation model with multiple languages at the source and one language at the target. Multiple languages with different word orders participate in the model training at the same time, so that the model can learn mul

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Jabar, H. Yousif. "Neural Computing based Part of Speech Tagger for Arabic Language: A review study." International Journal of Computation and Applied Sciences IJOCAAS 1, no. 5 (2020): 361–65. https://doi.org/10.5281/zenodo.4002418.

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this paper aims to explore the implementation of part of speech tagger (POS) for Arabic Language using neural computing. The Arabic Language is one of the most important languages in the world. More than 422 million people use the Arabic Language as the primary media for writing and speaking. The part of speech is one crucial stage for most natural languages processing. Many factors affect the performance of POS including the type of language, the corpus size, the tag-set, the computation model. The artificial neural network (ANN) is modern paradigms that simulate the human behavior to learn,

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Deepak Mane. "Transformer based Neural Network Architecturefor Regional Language Translation." Advances in Nonlinear Variational Inequalities 28, no. 3s (2024): 211–25. https://doi.org/10.52783/anvi.v28.2925.

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Today, the importance of regional language translation has grown significantly as it facilitates effective communication, accessibility, and inclusivity for diverse populations in a globalized world. It enables individuals to access information and services in their native languages, fosters cultural preservation, and enhances opportunities for education and economic growth. So, this paper presents a thorough implementation of the Transformer-based neural network architecture for regional language translation. Our methodology enables translation between languages by utilizing cross-attention a

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Wu, Yi-Chao, Fei Yin, and Cheng-Lin Liu. "Improving handwritten Chinese text recognition using neural network language models and convolutional neural network shape models." Pattern Recognition 65 (May 2017): 251–64. http://dx.doi.org/10.1016/j.patcog.2016.12.026.

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Babić, Karlo, Sanda Martinčić-Ipšić, and Ana Meštrović. "Survey of Neural Text Representation Models." Information 11, no. 11 (2020): 511. http://dx.doi.org/10.3390/info11110511.

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In natural language processing, text needs to be transformed into a machine-readable representation before any processing. The quality of further natural language processing tasks greatly depends on the quality of those representations. In this survey, we systematize and analyze 50 neural models from the last decade. The models described are grouped by the architecture of neural networks as shallow, recurrent, recursive, convolutional, and attention models. Furthermore, we categorize these models by representation level, input level, model type, and model supervision. We focus on task-independ

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Muhammad, Murad, Shahzad Muhammad, and Fareed Naheeda. "Research Comparative Analysis of OCR Models for Urdu Language Characters Recognition." LC International Journal of STEM 5, no. 3 (2024): 55–63. https://doi.org/10.5281/zenodo.14028816.

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There have been many research works to digitalize Urdu Characters through machine learning algorithms. The algorithms that were already used for Urdu Optical Character Recognition [OCR] are Convolutional Neural Network [CNN], Recurrent Neural Network [RNN], and Transformer etc. There are also many machine learning algorithms that have not been used for Urdu OCR e.g Support Vector Machine, Graph Neural Network etc. This research paper proposes a comparative study between the performances of the already implemented Urdu OCR on some of following algorithms like Convolutional Neural Network/ Trans

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Hahn, Michael. "Theoretical Limitations of Self-Attention in Neural Sequence Models." Transactions of the Association for Computational Linguistics 8 (July 2020): 156–71. http://dx.doi.org/10.1162/tacl_a_00306.

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Transformers are emerging as the new workhorse of NLP, showing great success across tasks. Unlike LSTMs, transformers process input sequences entirely through self-attention. Previous work has suggested that the computational capabilities of self-attention to process hierarchical structures are limited. In this work, we mathematically investigate the computational power of self-attention to model formal languages. Across both soft and hard attention, we show strong theoretical limitations of the computational abilities of self-attention, finding that it cannot model periodic finite-state langu

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