Relevant bibliographies by topics / Neural Language Model

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Academic literature on the topic 'Neural Language Model'

Author: Grafiati
Published: 4 June 2021
Last updated: 27 April 2022

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Journal articles on the topic "Neural Language Model"

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Emami, Ahmad, and Frederick Jelinek. "A Neural Syntactic Language Model." Machine Learning 60, no. 1-3 (June 2, 2005): 195–227. http://dx.doi.org/10.1007/s10994-005-0916-y.

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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 neural lattice language models that utilize polysemous embeddings are able to improve perplexity by 9.95% relative to a word-level baseline, and that a Chinese model that handles multi-character tokens is able to improve perplexity by 20.94% relative to a character-level baseline.
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Zhang, Yike, Pengyuan Zhang, and Yonghong Yan. "Tailoring an Interpretable Neural Language Model." IEEE/ACM Transactions on Audio, Speech, and Language Processing 27, no. 7 (July 2019): 1164–78. http://dx.doi.org/10.1109/taslp.2019.2913087.

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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 multilingual transliteration significantly outperforms bilingual transliteration in different scenarios (average increase of 58% across a variety of languages we experimented with). We also show that multilingual transliteration models can generalize well to languages/language pairs not encountered during training and hence perform well on the zeroshot transliteration task. We show that further improvements can be achieved by using phonetic feature input.
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Tang, Zhiyuan, Dong Wang, Yixiang Chen, Lantian Li, and Andrew Abel. "Phonetic Temporal Neural Model for Language Identification." IEEE/ACM Transactions on Audio, Speech, and Language Processing 26, no. 1 (January 2018): 134–44. http://dx.doi.org/10.1109/taslp.2017.2764271.

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Souri, Adnan, Mohammed Al Achhab, Badr Eddine Elmohajir, and Abdelali Zbakh. "Neural network dealing with Arabic language." International Journal of Informatics and Communication Technology (IJ-ICT) 9, no. 2 (August 1, 2020): 73. http://dx.doi.org/10.11591/ijict.v9i2.pp73-82.

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Artificial Neural Networks have proved their efficiency in a large number of research domains. In this paper, we have applied Artificial Neural Networks on Arabic text to prove correct language modeling, text generation, and missing text prediction. In one hand, we have adapted Recurrent Neural Networks architectures to model Arabic language in order to generate correct Arabic sequences. In the other hand, Convolutional Neural Networks have been parameterized, basing on some specific features of Arabic, to predict missing text in Arabic documents. We have demonstrated the power of our adapted models in generating and predicting correct Arabic text comparing to the standard model. The model had been trained and tested on known free Arabic datasets. Results have been promising with sufficient accuracy.
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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 (April 3, 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 classical neural models for cross-lingual natural language inference. It trains on the union of data in the source language and data in the target language, learning language-invariant features to improve the inference performance. Experimental results on the XNLI benchmark demonstrate that three popular neural models enhanced by the proposed framework significantly outperform the original models.
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Ferreira, Pedro M., Diogo Pernes, Ana Rebelo, and Jaime S. Cardoso. "Signer-Independent Sign Language Recognition with Adversarial Neural Networks." International Journal of Machine Learning and Computing 11, no. 2 (March 2021): 121–29. http://dx.doi.org/10.18178/ijmlc.2021.11.2.1024.

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Sign Language Recognition (SLR) has become an appealing topic in modern societies because such technology can ideally be used to bridge the gap between deaf and hearing people. Although important steps have been made towards the development of real-world SLR systems, signer-independent SLR is still one of the bottleneck problems of this research field. In this regard, we propose a deep neural network along with an adversarial training objective, specifically designed to address the signer-independent problem. Specifically, the proposed model consists of an encoder, mapping from input images to latent representations, and two classifiers operating on these underlying representations: (i) the sign-classifier, for predicting the class/sign labels, and (ii) the signer-classifier, for predicting their signer identities. During the learning stage, the encoder is simultaneously trained to help the sign-classifier as much as possible while trying to fool the signer-classifier. This adversarial training procedure allows learning signer-invariant latent representations that are in fact highly discriminative for sign recognition. Experimental results demonstrate the effectiveness of the proposed model and its capability of dealing with the large inter-signer variations.
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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 (September 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 probabilistic context-free grammar, language models based on Simon/Pitman-Yor processes, neural language models, and generative adversarial networks for text generation. Our analysis reveals that language models based on recurrent neural networks with a gating mechanism (i.e., long short-term memory; a gated recurrent unit; and quasi-recurrent neural networks) are the only computational models that can reproduce the long memory behavior of natural language. Furthermore, through comparison with recently proposed model-based evaluation methods, we find that the exponent of Taylor’s law is a good indicator of model quality.
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P., Dr Karrupusamy. "Analysis of Neural Network Based Language Modeling." March 2020 2, no. 1 (March 30, 2020): 53–63. http://dx.doi.org/10.36548/jaicn.2020.1.006.

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The fundamental and core process of the natural language processing is the language modelling usually referred as the statistical language modelling. The language modelling is also considered to be vital in the processing the natural languages as the other chores such as the completion of sentences, recognition of speech automatically, translations of the statistical machines, and generation of text and so on. The success of the viable natural language processing totally relies on the quality of the modelling of the language. In the previous spans the research field such as the linguistics, psychology, speech recognition, data compression, neuroscience, machine translation etc. As the neural network are the very good choices for having a quality language modelling the paper presents the analysis of neural networks in the modelling of the language. Utilizing some of the dataset such as the Penn Tree bank, Billion Word Benchmark and the Wiki Test the neural network models are evaluated on the basis of the word error rate, perplexity and the bilingual evaluation under study scores to identify the optimal model.
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Dissertations / Theses on the topic "Neural Language Model"

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Rolnic, Sergiu Gabriel. "Anonimizzazione di documenti mediante Named Entity Recognition e Neural Language Model." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2022.

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I transformers hanno rivoluzionato il mondo dell'interpretazione linguistica da parte delle macchine. La possibilità di addestrare un neural language model su vocabolari ed enciclopedie intere, per poi utilizzare le conoscenze acquisite e trasmetterle a task specifici, ha permesso di raggiungere lo stato dell'arte in quasi tutti i domini applicativi del Natural Language Processing. In questo contesto è stato sviluppato un applicativo per l'anonimizzazione di file, in grado di identificare entità specifiche rappresentative di dati personali.
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Le, Hai Son. "Continuous space models with neural networks in natural language processing." Phd thesis, Université Paris Sud - Paris XI, 2012. http://tel.archives-ouvertes.fr/tel-00776704.

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The purpose of language models is in general to capture and to model regularities of language, thereby capturing morphological, syntactical and distributional properties of word sequences in a given language. They play an important role in many successful applications of Natural Language Processing, such as Automatic Speech Recognition, Machine Translation and Information Extraction. The most successful approaches to date are based on n-gram assumption and the adjustment of statistics from the training data by applying smoothing and back-off techniques, notably Kneser-Ney technique, introduced twenty years ago. In this way, language models predict a word based on its n-1 previous words. In spite of their prevalence, conventional n-gram based language models still suffer from several limitations that could be intuitively overcome by consulting human expert knowledge. One critical limitation is that, ignoring all linguistic properties, they treat each word as one discrete symbol with no relation with the others. Another point is that, even with a huge amount of data, the data sparsity issue always has an important impact, so the optimal value of n in the n-gram assumption is often 4 or 5 which is insufficient in practice. This kind of model is constructed based on the count of n-grams in training data. Therefore, the pertinence of these models is conditioned only on the characteristics of the training text (its quantity, its representation of the content in terms of theme, date). Recently, one of the most successful attempts that tries to directly learn word similarities is to use distributed word representations in language modeling, where distributionally words, which have semantic and syntactic similarities, are expected to be represented as neighbors in a continuous space. These representations and the associated objective function (the likelihood of the training data) are jointly learned using a multi-layer neural network architecture. In this way, word similarities are learned automatically. This approach has shown significant and consistent improvements when applied to automatic speech recognition and statistical machine translation tasks. A major difficulty with the continuous space neural network based approach remains the computational burden, which does not scale well to the massive corpora that are nowadays available. For this reason, the first contribution of this dissertation is the definition of a neural architecture based on a tree representation of the output vocabulary, namely Structured OUtput Layer (SOUL), which makes them well suited for large scale frameworks. The SOUL model combines the neural network approach with the class-based approach. It achieves significant improvements on both state-of-the-art large scale automatic speech recognition and statistical machine translations tasks. The second contribution is to provide several insightful analyses on their performances, their pros and cons, their induced word space representation. Finally, the third contribution is the successful adoption of the continuous space neural network into a machine translation framework. New translation models are proposed and reported to achieve significant improvements over state-of-the-art baseline systems.
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Keisala, Simon. "Using a Character-Based Language Model for Caption Generation." Thesis, Linköpings universitet, Interaktiva och kognitiva system, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-163001.

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Using AI to automatically describe images is a challenging task. The aim of this study has been to compare the use of character-based language models with one of the current state-of-the-art token-based language models, im2txt, to generate image captions, with focus on morphological correctness. Previous work has shown that character-based language models are able to outperform token-based language models in morphologically rich languages. Other studies show that simple multi-layered LSTM-blocks are able to learn to replicate the syntax of its training data. To study the usability of character-based language models an alternative model based on TensorFlow im2txt has been created. The model changes the token-generation architecture into handling character-sized tokens instead of word-sized tokens. The results suggest that a character-based language model could outperform the current token-based language models, although due to time and computing power constraints this study fails to draw a clear conclusion. A problem with one of the methods, subsampling, is discussed. When using the original method on character-sized tokens this method removes characters (including special characters) instead of full words. To solve this issue, a two-phase approach is suggested, where training data first is separated into word-sized tokens where subsampling is performed. The remaining tokens are then separated into character-sized tokens. Future work where the modified subsampling and fine-tuning of the hyperparameters are performed is suggested to gain a clearer conclusion of the performance of character-based language models.
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Gorana, Mijatović. "Dekompozicija neuralne aktivnosti: model za empirijsku karakterizaciju inter-spajk intervala." Phd thesis, Univerzitet u Novom Sadu, Fakultet tehničkih nauka u Novom Sadu, 2018. https://www.cris.uns.ac.rs/record.jsf?recordId=107498&source=NDLTD&language=en.

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Disertacija se se bavi analizom mogućnosti brze, efikasnei pouzdane klasterizacije masivnog skupa neuralnihsnimaka na osnovu probabilističkih parametara procenjenihiz obrazaca generisanja akcionih potencijala, tzv."spajkova", na izlazu pojedinih neurona. Neuralnaaktivnost se grubo može podeliti na periode intezivne,umerene i niske aktivnosti. Shodno tome, predložena jegruba dekompozicija neuralne aktivnosti na tri moda kojaodgovaraju navedenim obrascima neuralne aktivnosti, naosnovu dobro poznatog Gilbert-Eliot modela. Modovi sudodatno raščlanjeni na sopstvena stanja na osnovu osobina sukcesivnih spajkova, omogućujući finiji, kompozitniopis neuralne aktivnosti. Za svaki neuron empirijski seprocenjuju probabilistički parametri grube dekompozicije- na osnovu Gilbert-Eliotovog modela i finije dekompozicije- na osnovu sopstvenih stanja modova, obezbeđujućiželjeni skup deskriptora. Dobijeni deskriptorikoriste se kao obeležja nekoliko algoritama klasterizacijenad simuliranim i eksperimentalnim podacima. Za generisanjesimuliranih podataka primenjen je jednostavanmodel za generisanje akcionih potencijala različitihoscilatornih ponašanja pobuđujućih i blokirajućih kortikalnihneurona. Validacija primene probabilističkih parametaraza klasterizaciju rada neurona izvršena je naosnovu estimacije parametera nad generisanim neuralnimodzivima. Eksperimentalni podaci su dobijenisnimanjem kortikografskih signala iz dorzalnog anteriornogcingularanog korteksa i lateralnog prefrontalnogkorteksa korteksa budnih rezus majmuna. U okviru predloženogprotokola evaluacije različitih pristupaklasterizacije testirano je nekoliko metoda. Klasterizacijazasnovana na akumulaciji dokaza iz ansambla particijadobijenih k-means klasterovanjem dala je najstabilnijegrupisanje neuralnih jedinica uz brzu i efikasnu implementaciju.Predložena empirijska karakterizacija može daposluži za identifikaciju korelacije sa spoljašnjim stimulusima,akcijama i ponašanjem životinja u okvirueksperimentalne procedure. Prednosti ovog postupka zaopis neuralne aktivnosti su brza estimacija i mali skupdeskriptora. Računarska efikasnost omogućuje primenunad obimnim, paralelno snimanim neuralnim podacima utoku snimanja ili u periodima od interesa za identifikacijuaktiviranih i povezanih zona pri određenim aktivnostima.
The advances in extracellular neural recording techniquesresult in big data volumes that necessitate fast,reliable, and automatic identification of statisticallysimilar units. This study proposes a single frameworkyielding a compact set of probabilistic descriptors thatcharacterise the firing patterns of a single unit. Probabilisticfeatures are estimated from an inter-spikeintervaltime series, without assumptions about the firing distribution or the stationarity. The first level of proposedfiring patterns decomposition divides the inter-spikeintervals into bursting, moderate and idle firing modes,yielding a coarse feature set. The second level identifiesthe successive bursting spikes, or the spiking acceleration/deceleration in the moderate firing mode, yieldinga refined feature set. The features are estimated fromsimulated data and from experimental recordings fromthe lateral prefrontal cortex in awake, behaving rhesusmonkeys. An effcient and stable partitioning of neuralunits is provided by the ensemble evidence accumulationclustering. The possibility of selecting the number ofclusters and choosing among coarse and refined featuresets provides an opportunity to explore and comparedifferent data partitions. The estimation of features, ifapplied to a single unit, can serve as a tool for the firinganalysis, observing either overall spiking activity or theperiods of interest in trial-to-trial recordings. If applied tomassively parallel recordings, it additionally serves as aninput to the clustering procedure, with the potential tocompare the functional properties of various brainstructures and to link the types of neural cells to theparticular behavioural states.
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Garagnani, Max. "Understanding language and attention : brain-based model and neurophysiological experiments." Thesis, University of Cambridge, 2009. https://www.repository.cam.ac.uk/handle/1810/243852.

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This work concerns the investigation of the neuronal mechanisms at the basis of language acquisition and processing, and the complex interactions of language and attention processes in the human brain. In particular, this research was motivated by two sets of existing neurophysiological data which cannot be reconciled on the basis of current psycholinguistic accounts: on the one hand, the N400, a robust index of lexico-semantic processing which emerges at around 400ms after stimulus onset in attention demanding tasks and is larger for senseless materials (meaningless pseudowords) than for matched meaningful stimuli (words); on the other, the more recent results on the Mismatch Negativity (MMN, latency 100-250ms), an early automatic brain response elicited under distraction which is larger to words than to pseudowords. We asked what the mechanisms underlying these differential neurophysiological responses may be, and whether attention and language processes could interact so as to produce the observed brain responses, having opposite magnitude and different latencies. We also asked questions about the functional nature and anatomical characteristics of the cortical representation of linguistic elements. These questions were addressed by combining neurocomputational techniques and neuroimaging (magneto-encephalography, MEG) experimental methods. Firstly, a neurobiologically realistic neural-network model composed of neuron-like elements (graded response units) was implemented, which closely replicates the neuroanatomical and connectivity features of the main areas of the left perisylvian cortex involved in spoken language processing (i.e., the areas controlling speech output – left inferior-prefrontal cortex, including Broca’s area – and the main sensory input – auditory – areas, located in the left superior-temporal lobe, including Wernicke’s area). Secondly, the model was used to simulate early word acquisition processes by means of a Hebbian correlation learning rule (which reflects known synaptic plasticity mechanisms of the neocortex). The network was “taught” to associate pairs of auditory and articulatory activation patterns, simulating activity due to perception and production of the same speech sound: as a result, neuronal word representations distributed over the different cortical areas of the model emerged. Thirdly, the network was stimulated, in its “auditory cortex”, with either one of the words it had learned, or new, unfamiliar pseudoword patterns, while the availability of attentional resources was modulated by changing the level of non-specific, global cortical inhibition. In this way, the model was able to replicate both the MMN and N400 brain responses by means of a single set of neuroscientifically grounded principles, providing the first mechanistic account, at the cortical-circuit level, for these data. Finally, in order to verify the neurophysiological validity of the model, its crucial predictions were tested in a novel MEG experiment investigating how attention processes modulate event-related brain responses to speech stimuli. Neurophysiological responses to the same words and pseudowords were recorded while the same subjects were asked to attend to the spoken input or ignore it. The experimental results confirmed the model’s predictions; in particular, profound variability of magnetic brain responses to pseudowords but relative stability of activation to words as a function of attention emerged. While the results of the simulations demonstrated that distributed cortical representations for words can spontaneously emerge in the cortex as a result of neuroanatomical structure and synaptic plasticity, the experimental results confirm the validity of the model and provide evidence in support of the existence of such memory circuits in the brain. This work is a first step towards a mechanistic account of cognition in which the basic atoms of cognitive processing (e.g., words, objects, faces) are represented in the brain as discrete and distributed action-perception networks that behave as closed, independent systems.
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Miao, Yishu. "Deep generative models for natural language processing." Thesis, University of Oxford, 2017. http://ora.ox.ac.uk/objects/uuid:e4e1f1f9-e507-4754-a0ab-0246f1e1e258.

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Deep generative models are essential to Natural Language Processing (NLP) due to their outstanding ability to use unlabelled data, to incorporate abundant linguistic features, and to learn interpretable dependencies among data. As the structure becomes deeper and more complex, having an effective and efficient inference method becomes increasingly important. In this thesis, neural variational inference is applied to carry out inference for deep generative models. While traditional variational methods derive an analytic approximation for the intractable distributions over latent variables, here we construct an inference network conditioned on the discrete text input to provide the variational distribution. The powerful neural networks are able to approximate complicated non-linear distributions and grant the possibilities for more interesting and complicated generative models. Therefore, we develop the potential of neural variational inference and apply it to a variety of models for NLP with continuous or discrete latent variables. This thesis is divided into three parts. Part I introduces a generic variational inference framework for generative and conditional models of text. For continuous or discrete latent variables, we apply a continuous reparameterisation trick or the REINFORCE algorithm to build low-variance gradient estimators. To further explore Bayesian non-parametrics in deep neural networks, we propose a family of neural networks that parameterise categorical distributions with continuous latent variables. Using the stick-breaking construction, an unbounded categorical distribution is incorporated into our deep generative models which can be optimised by stochastic gradient back-propagation with a continuous reparameterisation. Part II explores continuous latent variable models for NLP. Chapter 3 discusses the Neural Variational Document Model (NVDM): an unsupervised generative model of text which aims to extract a continuous semantic latent variable for each document. In Chapter 4, the neural topic models modify the neural document models by parameterising categorical distributions with continuous latent variables, where the topics are explicitly modelled by discrete latent variables. The models are further extended to neural unbounded topic models with the help of stick-breaking construction, and a truncation-free variational inference method is proposed based on a Recurrent Stick-breaking construction (RSB). Chapter 5 describes the Neural Answer Selection Model (NASM) for learning a latent stochastic attention mechanism to model the semantics of question-answer pairs and predict their relatedness. Part III discusses discrete latent variable models. Chapter 6 introduces latent sentence compression models. The Auto-encoding Sentence Compression Model (ASC), as a discrete variational auto-encoder, generates a sentence by a sequence of discrete latent variables representing explicit words. The Forced Attention Sentence Compression Model (FSC) incorporates a combined pointer network biased towards the usage of words from source sentence, which significantly improves the performance when jointly trained with the ASC model in a semi-supervised learning fashion. Chapter 7 describes the Latent Intention Dialogue Models (LIDM) that employ a discrete latent variable to learn underlying dialogue intentions. Additionally, the latent intentions can be interpreted as actions guiding the generation of machine responses, which could be further refined autonomously by reinforcement learning. Finally, Chapter 8 summarizes our findings and directions for future work.
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Al-Kadhimi, Staffan, and Paul Löwenström. "Identification of machine-generated reviews : 1D CNN applied on the GPT-2 neural language model." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-280335.

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With recent advances in machine learning, computers are able to create more convincing text, creating a concern for an increase in fake information on the internet. At the same time, researchers are creating tools for detecting computer-generated text. Researchers have been able to exploit flaws in neural language models and use them against themselves; for example, GLTR provides human users with a visual representation of texts that assists in classification as human-written or machine-generated. By training a convolutional neural network (CNN) on GLTR output data from analysis of machine-generated and human-written movie reviews, we are able to take GLTR a step further and use it to automatically perform this classification. However, using a CNN with GLTR as the main source of data for classification does not appear to be enough to be on par with the best existing approaches.
I och med de senaste framstegen inom maskininlärning kan datorer skapa mer och mer övertygande text, vilket skapar en oro för ökad falsk information på internet. Samtidigt vägs detta upp genom att forskare skapar verktyg för att identifiera datorgenererad text. Forskare har kunnat utnyttja svagheter i neurala språkmodeller och använda dessa mot dem. Till exempel tillhandahåller GLTR användare en visuell representation av texter, som hjälp för att klassificera dessa som människo- skrivna eller maskingenererade. Genom att träna ett faltningsnätverk (convolutional neural network, eller CNN) på utdata från GLTR-analys av maskingenererade och människoskrivna filmrecensioner, tar vi GLTR ett steg längre och använder det för att genomföra klassifikationen automatiskt. Emellertid tycks det ej vara tillräckligt att använda en CNN med GLTR som huvuddatakälla för att klassificera på en nivå som är jämförbar med de bästa existerande metoderna.
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Roos, Magnus. "Speech Comprehension : Theoretical approaches and neural correlates." Thesis, Högskolan i Skövde, Institutionen för biovetenskap, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-11240.

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This review has examined the spatial and temporal neural activation of speech comprehension. Six theories on speech comprehension were selected and reviewed. The most fundamental structures for speech comprehension are the superior temporal gyrus, the fusiform gyrus, the temporal pole, the temporoparietal junction, and the inferior frontal gyrus. Considering temporal aspects of processes, the N400 ERP effect indicates semantic violations, and the P600 indicates re-evaluation of a word due to ambiguity or syntax error. The dual-route processing model provides the most accurate account of neural correlates and streams of activation necessary for speech comprehension, while also being compatible with both the reviewed studies and the reviewed theories. The integrated theory of language production and comprehension provides a contemporary theory of speech production and comprehension with roots in computational neuroscience, which in conjunction with the dual-route processing model could drive the fields of language and neuroscience even further forward.
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Cavallucci, Martina. "Speech Recognition per l'italiano: Sviluppo e Sperimentazione di Soluzioni Neurali con Language Model." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2022.

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Le e-mail e i servizi di messaggistica hanno cambiato significativamente la comunicazione umana, ma la parola è ancora il metodo più importante di comunicazione tra esseri umani. Pertanto, il riconoscimento vocale automatico (ASR) è di particolare rilevanza perché fornisce una trascrizione della lingua parlata che può essere valutata da sistemi automatizzati. Con altoparlanti intelligenti come Google Home, Alexa o Siri, l' ASR è già un parte integrante di molte famiglie ed è usato per suonare musica, rispondere alle domande o controllare altri dispositivi intelligenti come un sistema di domotica. Tuttavia, l' ASR può essere trovato anche in molti altri sistemi, come sistemi di dettatura, traduttori vocali o interfacce utente vocali. Sempre più aziende ne comprendono le potenzialità sopratutto per migliorare i processi aziendali, il lavoro di tesi mira infatti a sperimentare modelli neurali per la trascrizione di Webinar creati dall'azienda ospitante Maggioli dove si è svolto il tirocinio, ottenendo così trascrizioni utili per il recupero delle informazioni e la loro gestione. A tale scopo si sono utilizzati modelli basati sui recenti Transformers e grazie alla tecnica dell'apprendimento auto-supervisionato che apprende da dati non etichettati è stato possibile ottenere buoni risultati su dataset con audio e trascrizioni in italiano di cui si dispongono ancora poche risorse rispetto alla lingua inglese.
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Rossi, Alex. "Self-supervised information retrieval: a novel approach based on Deep Metric Learning and Neural Language Models." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021.

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Most of the existing open-source search engines, utilize keyword or tf-idf based techniques to find relevant documents and web pages relative to an input query. Although these methods, with the help of a page rank or knowledge graphs, proved to be effective in some cases, they often fail to retrieve relevant instances for more complicated queries that would require a semantic understanding to be exploited. In this Thesis, a self-supervised information retrieval system based on transformers is employed to build a semantic search engine over the library of Gruppo Maggioli company. Semantic search or search with meaning can refer to an understanding of the query, instead of simply finding words matches and, in general, it represents knowledge in a way suitable for retrieval. We chose to investigate a new self-supervised strategy to handle the training of unlabeled data based on the creation of pairs of ’artificial’ queries and the respective positive passages. We claim that by removing the reliance on labeled data, we may use the large volume of unlabeled material on the web without being limited to languages or domains where labeled data is abundant.
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Books on the topic "Neural Language Model"

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Miikkulainen, Risto. Subsymbolic natural language processing: An integrated model of scripts, lexicon, and memory. Cambridge, Mass: MIT Press, 1993.

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Ratcliff, Roger, and Philip Smith. Modeling Simple Decisions and Applications Using a Diffusion Model. Edited by Jerome R. Busemeyer, Zheng Wang, James T. Townsend, and Ami Eidels. Oxford University Press, 2015. http://dx.doi.org/10.1093/oxfordhb/9780199957996.013.3.

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The diffusion model is one of the major sequential-sampling models for two-choice decision-making and choice response time in psychology. The model conceives of decision-making as a process in which noisy evidence is accumulated until one of two response criteria is reached and the associated response is made. The criteria represent the amount of evidence needed to make each decision and reflect the decision maker’s response biases and speed-accuracy trade-off settings. In this chapter we examine the application of the diffusion model in a variety of different settings. We discuss the optimality of the model and review its applications to a number of cognitive tasks, including perception, memory, and language tasks. We also consider its applications to normal and special populations, to the cognitive foundations of individual differences, to value-based decisions, and its role in understanding the neural basis of decision-making.
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Cairns, Paul, Joseph P. Levy, Dimitrios Bairaktaris, and John A. Bullinaria. Connectionist Models of Memory and Language. Taylor & Francis Group, 2015.

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Bergen, Benjamin, and Nancy Chang. Embodied Construction Grammar. Edited by Thomas Hoffmann and Graeme Trousdale. Oxford University Press, 2013. http://dx.doi.org/10.1093/oxfordhb/9780195396683.013.0010.

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This chapter focuses on Embodied Construction Grammar (ECG), another computational implementation of Construction Grammar. It points out that the driving question of this framework is how language is used in actual physical and social contexts, and explains that ECG is an attempt to computationally model the cognitive and neural mechanisms that underlie human linguistic behavior. The chapter evaluates the role of mental simulation in processing and outlines how language can be seen as in interface to simulation. It also shows how constructions are represented in ECG and describes an ECG-based model of language comprehension.
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Strevens, Michael. The Whole Story. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199685509.003.0005.

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Causal explanations in the high-level sciences typically black-box the low-level details of the causal mechanisms that they invoke to account for their explananda: economists’ black-box psychological processes, psychologists’ black-box neural processes, and so on. Are these black-boxing explanatory models complete explanations of the phenomena in question, or are they just sketches of or templates for the whole explanatory story? This chapter poses a focused version of the question in the context of convergent evolution, the existence of which appears to show that underlying mechanisms are completely irrelevant to the explanation of high-level biological features, including perhaps thought and language—in which case a black-boxing model would be a complete explanation of such features rather than a mere sketch. Arguments for and against such a model’s explanatory completeness are considered; the chapter comes down tentatively against.
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1957-, Houghton George, ed. Connectionist models in cognitive psychology. Hove: Psychology Press, 2004.

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Connectionist Models in Cognitive Psychology. Taylor & Francis Group, 2014.

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Papanicolaou, Andrew C., and Marina Kilintari. Imaging the Networks of Language. Edited by Andrew C. Papanicolaou. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199764228.013.15.

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Among the “higher” functions, language and its cerebral networks is the most intensively explored through behavioral or clinical studies and, more recently, through functional neuroimaging. From the former studies, several models (only partially congruent) have emerged during the past three centuries regarding the organization and topography of the brain mechanisms of the acoustic, phonological, semantic, syntactic, and pragmatic operations in which psycholinguists have divided the language function. The main task of this chapter is to extract from the vast functional neuroimaging literature of language reliable evidence that would be used to disconfirm the various hypotheses comprising the current language models. Most of these hypotheses concern the anatomical structures that could be considered nodes or hubs of the neuronal networks mediating the above-mentioned linguistic operations. Using the same criteria, the authors present neuroimaging evidence relevant to the issue of the neuronal mediation of sign languages, reading, and dyslexia.
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Gomez-Perez, Jose Manuel, Ronald Denaux, and Andres Garcia-Silva. A Practical Guide to Hybrid Natural Language Processing: Combining Neural Models and Knowledge Graphs for NLP. Springer, 2020.

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McNamara, Patrick, and Magda Giordano. Cognitive Neuroscience and Religious Language. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190636647.003.0005.

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Communication between deities and human beings rests on the use of language. Religious language has peculiarities such as the use of a formal voice, reductions in first-person and elevation of third-person pronoun use, archaistic elements, and an abundance of speech acts—features that reflect and facilitate the binding of the individual to conceived ultimate reality and value, decentering the Self while focusing on the deity. Explorations of the neurologic correlates of these cognitive and linguistic processes may be useful to identify constraints on neurocognitive models of religious language, and metaphor. The key brain regions that may mediate religious language include neural networks known to be involved in computational assessments of value, future-oriented simulations, Self-agency, Self-reflection, and attributing intentionality of goals to others. Studies indicate that some of the areas involved in those processes are active during personal prayer, whereas brain regions related to habit formation appear active during formal prayer. By examining religious language, and the brain areas engaged by it, we aim to develop more comprehensive neurocognitive models of religious cognition.
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Book chapters on the topic "Neural Language Model"

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Soutner, Daniel, Zdeněk Loose, Luděk Müller, and Aleš Pražák. "Neural Network Language Model with Cache." In Text, Speech and Dialogue, 528–34. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-32790-2_64.

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Kurimo, Mikko, and Krista Lagus. "An Efficiently Focusing Large Vocabulary Language Model." In Artificial Neural Networks — ICANN 2002, 1068–73. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-46084-5_173.

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Ebisu, Takuma, and Ryutaro Ichise. "Representation of Relations by Planes in Neural Network Language Model." In Neural Information Processing, 300–307. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46687-3_33.

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Ma, Dehong, Sujian Li, and Houfeng Wang. "Target Extraction via Feature-Enriched Neural Networks Model." In Natural Language Processing and Chinese Computing, 353–64. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99495-6_30.

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Zhou, Long, Jiajun Zhang, Yang Zhao, and Chengqing Zong. "Non-autoregressive Neural Machine Translation with Distortion Model." In Natural Language Processing and Chinese Computing, 403–15. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60450-9_32.

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Hao, Bin, Min Zhang, Weizhi Ma, Shaoyun Shi, Xinxing Yu, Houzhi Shan, Yiqun Liu, and Shaoping Ma. "Negative Feedback Aware Hybrid Sequential Neural Recommendation Model." In Natural Language Processing and Chinese Computing, 279–91. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60457-8_23.

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Christie, William M. "2. A Neural Network Model of Language Production." In Functional Approaches to Language, Culture and Cognition, 23. Amsterdam: John Benjamins Publishing Company, 2000. http://dx.doi.org/10.1075/cilt.163.07chr.

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Lin, Li, Jin Liu, Zhenkai Gu, Zelun Zhang, and Haoliang Ren. "Build Chinese Language Model with Recurrent Neural Network." In Advances in Computer Science and Ubiquitous Computing, 920–25. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-7605-3_146.

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Athanaselis, Theologos, Stelios Bakamidis, and Ioannis Dologlou. "A Fast Algorithm for Words Reordering Based on Language Model." In Artificial Neural Networks – ICANN 2006, 943–51. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11840930_98.

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Panchev, Christo. "A Spiking Neural Network Model of Multi-modal Language Processing of Robot Instructions." In Biomimetic Neural Learning for Intelligent Robots, 182–210. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11521082_11.

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Conference papers on the topic "Neural Language Model"

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Yu, Seunghak, Nilesh Kulkarni, Haejun Lee, and Jihie Kim. "Syllable-level Neural Language Model for Agglutinative Language." In Proceedings of the First Workshop on Subword and Character Level Models in NLP. Stroudsburg, PA, USA: Association for Computational Linguistics, 2017. http://dx.doi.org/10.18653/v1/w17-4113.

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Lau, Jey Han, Timothy Baldwin, and Trevor Cohn. "Topically Driven Neural Language Model." In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Stroudsburg, PA, USA: Association for Computational Linguistics, 2017. http://dx.doi.org/10.18653/v1/p17-1033.

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Huang, Jiaji, Yi Li, Wei Ping, and Liang Huang. "Large Margin Neural Language Model." In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, PA, USA: Association for Computational Linguistics, 2018. http://dx.doi.org/10.18653/v1/d18-1150.

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Wu, Liwei, Youhua Wu, Fei Li, and Tao Zheng. "An Improved Recurrent Neural Network Language Model for Programming Language." In 2019 International Joint Conference on Neural Networks (IJCNN). IEEE, 2019. http://dx.doi.org/10.1109/ijcnn.2019.8852433.

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Chowdhury, Hemayet Ahmed, Md Azizul Haque Imon, Anisur Rahman, Aisha Khatun, and Md Saiful Islam. "A Continuous Space Neural Language Model for Bengali Language." In 2019 22nd International Conference on Computer and Information Technology (ICCIT). IEEE, 2019. http://dx.doi.org/10.1109/iccit48885.2019.9038568.

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Chien, Jen-Tzung, and Yuan-Chu Ku. "Bayesian recurrent neural network language model." In 2014 IEEE Spoken Language Technology Workshop (SLT). IEEE, 2014. http://dx.doi.org/10.1109/slt.2014.7078575.

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Chien, Jen-Tzung, and Che-Yu Kuo. "Markov Recurrent Neural Network Language Model." In 2019 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU). IEEE, 2019. http://dx.doi.org/10.1109/asru46091.2019.9003850.

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Shi, YongZhe, Wei-Qiang Zhang, Meng Cai, and Jia Liu. "Temporal kernel neural network language model." In ICASSP 2013 - 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2013. http://dx.doi.org/10.1109/icassp.2013.6639273.

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Kawamae, Noriaki. "Topic Structure-Aware Neural Language Model." In The World Wide Web Conference. New York, New York, USA: ACM Press, 2019. http://dx.doi.org/10.1145/3308558.3313757.

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Alumäe, Tanel. "Multi-domain neural network language model." In Interspeech 2013. ISCA: ISCA, 2013. http://dx.doi.org/10.21437/interspeech.2013-515.

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Reports on the topic "Neural Language Model"

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Althoff, J. L., M. L. Apicella, and S. Singh. Integrated Information Support System (IISS). Volume 5. Common Data Model Subsystem. Part 5. Neutral Data Definition Language (NDDL) Development Specification. Fort Belvoir, VA: Defense Technical Information Center, September 1990. http://dx.doi.org/10.21236/ada252450.

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Althoff, J., and M. Apicella. Integrated Information Support System (IISS). Volume 5. Common Data Model Subsystem. Part 9. Neutral Data Manipulation Language (NDML) Precompiler Development Specification. Section 2. Fort Belvoir, VA: Defense Technical Information Center, September 1990. http://dx.doi.org/10.21236/ada252526.

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Apicella, M. L., J. Slaton, and B. Levi. Integrated Information Support System (IISS). Volume 5. Common Data Model Subsystem. Part 10. Neutral Data Manipulation Language (NDML) Precompiler Control Module Product Specification. Fort Belvoir, VA: Defense Technical Information Center, September 1990. http://dx.doi.org/10.21236/ada250451.

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Apicella, M. L., J. Slaton, and B. Levi. Integrated Information Support System (IISS). Volume 5. Common Data Model Subsystem. Part 13. Neutral Data Manipulation Language (NDML) Precompiler Parse NDML Product Specification. Fort Belvoir, VA: Defense Technical Information Center, September 1990. http://dx.doi.org/10.21236/ada250453.

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Althoff, J., M. Apicella, and S. Singh. Integrated Information Support System (IISS). Volume 5. Common Data Model Subsystem. Part 6. Neutral Data Definition Language (NDDL) Product Specification. Section 3 of 6. Fort Belvoir, VA: Defense Technical Information Center, September 1990. http://dx.doi.org/10.21236/ada251997.

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Althoff, J., M. Apicella, and S. Singh. Integrated Information Support System (IISS). Volume 5. Common Data Model Subsystem. Part 6. Neutral Data Definition Language (NDDL) Product Specification. Section 4 of 6. Fort Belvoir, VA: Defense Technical Information Center, September 1990. http://dx.doi.org/10.21236/ada251998.

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Althoff, J., M. Apicella, and S. Singh. Integrated Information Support System (IISS). Volume 5. Common Data Model Subsystem. Part 6. Neutral Data Definition Language (NDDL) Product Specification. Section 5 of 6. Fort Belvoir, VA: Defense Technical Information Center, September 1990. http://dx.doi.org/10.21236/ada251999.

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Althoff, J., M. Apicella, and S. Singh. Integrated Information Support System (IISS). Volume 5. Common Data Model Subsystem. Part 6. Neutral Data Definition Language (NDDL) Product Specification. Section 6 of 6. Fort Belvoir, VA: Defense Technical Information Center, September 1990. http://dx.doi.org/10.21236/ada252053.

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Apicella, M. L., J. Slaton, and B. Levi. Integrated Information Support System (IISS). Volume 5. Common Data Model Subsystem. Part 12. Neutral Data Manipulation Language (NDML) Precompiler Parse Procedure Division Product Specification. Fort Belvoir, VA: Defense Technical Information Center, September 1990. http://dx.doi.org/10.21236/ada250452.

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Apicella, M. L., J. Slaton, B. Levi, and A. Pashak. Integrated Information Support System (IISS). Volume 5. Common Data Model Subsystem. Part 23. Neutral Data Manipulation Language (NDML) Precompiler Build Source Code Product Specification. Fort Belvoir, VA: Defense Technical Information Center, September 1990. http://dx.doi.org/10.21236/ada250460.

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