Dissertations / Theses on the topic 'Language models'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 50 dissertations / theses for your research on the topic 'Language models.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse dissertations / theses on a wide variety of disciplines and organise your bibliography correctly.
Livingstone, Daniel Jack. "Computer models of the evolution of language and languages." Thesis, University of the West of Scotland, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.398331.
Full textRyder, Robin Jeremy. "Phylogenetic models of language diversification." Thesis, University of Oxford, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.543009.
Full textWaegner, Nicholas Paul. "Stochastic models for language acquisition." Thesis, University of Cambridge, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.309214.
Full textNiesler, Thomas Richard. "Category-based statistical language models." Thesis, University of Cambridge, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.627372.
Full textWallach, Hanna Megan. "Structured topic models for language." Thesis, University of Cambridge, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.612547.
Full textDouzon, Thibault. "Language models for document understanding." Electronic Thesis or Diss., Lyon, INSA, 2023. http://www.theses.fr/2023ISAL0075.
Full textEvery day, an uncountable amount of documents are received and processed by companies worldwide. In an effort to reduce the cost of processing each document, the largest companies have resorted to document automation technologies. In an ideal world, a document can be automatically processed without any human intervention: its content is read, and information is extracted and forwarded to the relevant service. The state-of-the-art techniques have quickly evolved in the last decades, from rule-based algorithms to statistical models. This thesis focuses on machine learning models for document information extraction. Recent advances in model architecture for natural language processing have shown the importance of the attention mechanism. Transformers have revolutionized the field by generalizing the use of attention and by pushing self-supervised pre-training to the next level. In the first part, we confirm that transformers with appropriate pre-training were able to perform document understanding tasks with high performance. We show that, when used as a token classifier for information extraction, transformers are able to exceptionally efficiently learn the task compared to recurrent networks. Transformers only need a small proportion of the training data to reach close to maximum performance. This highlights the importance of self-supervised pre-training for future fine-tuning. In the following part, we design specialized pre-training tasks, to better prepare the model for specific data distributions such as business documents. By acknowledging the specificities of business documents such as their table structure and their over-representation of numeric figures, we are able to target specific skills useful for the model in its future tasks. We show that those new tasks improve the model's downstream performances, even with small models. Using this pre-training approach, we are able to reach the performances of significantly bigger models without any additional cost during finetuning or inference. Finally, in the last part, we address one drawback of the transformer architecture which is its computational cost when used on long sequences. We show that efficient architectures derived from the classic transformer require fewer resources and perform better on long sequences. However, due to how they approximate the attention computation, efficient models suffer from a small but significant performance drop on short sequences compared to classical architectures. This incentivizes the use of different models depending on the input length and enables concatenating multimodal inputs into a single sequence
Townsend, Duncan Clarke McIntire. "Using a symbolic language parser to Improve Markov language models." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/100621.
Full textThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 31-32).
This thesis presents a hybrid approach to natural language processing that combines an n-gram (Markov) model with a symbolic parser. In concert these two techniques are applied to the problem of sentence simplification. The n-gram system is comprised of a relational database backend with a frontend application that presents a homogeneous interface for both direct n-gram lookup and Markov approximation. The query language exposed by the frontend also applies lexical information from the START natural language system to allow queries based on part of speech. Using the START natural language system's parser, English sentences are transformed into a collection of structural, syntactic, and lexical statements that are uniquely well-suited to the process of simplification. After reducing the parse of the sentence, the resulting expressions can be processed back into English. These reduced sentences are ranked by likelihood by the n-gram model.
by Duncan Clarke McIntire Townsend.
M. Eng.
Buttery, P. J. "Computational models for first language acquisition." Thesis, University of Cambridge, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.597195.
Full textNkadimeng, Calvin. "Language identification using Gaussian mixture models." Thesis, Stellenbosch : University of Stellenbosch, 2010. http://hdl.handle.net/10019.1/4170.
Full textENGLISH ABSTRACT: The importance of Language Identification for African languages is seeing a dramatic increase due to the development of telecommunication infrastructure and, as a result, an increase in volumes of data and speech traffic in public networks. By automatically processing the raw speech data the vital assistance given to people in distress can be speeded up, by referring their calls to a person knowledgeable in that language. To this effect a speech corpus was developed and various algorithms were implemented and tested on raw telephone speech data. These algorithms entailed data preparation, signal processing, and statistical analysis aimed at discriminating between languages. The statistical model of Gaussian Mixture Models (GMMs) were chosen for this research due to their ability to represent an entire language with a single stochastic model that does not require phonetic transcription. Language Identification for African languages using GMMs is feasible, although there are some few challenges like proper classification and accurate study into the relationship of langauges that need to be overcome. Other methods that make use of phonetically transcribed data need to be explored and tested with the new corpus for the research to be more rigorous.
AFRIKAANSE OPSOMMING: Die belang van die Taal identifiseer vir Afrika-tale is sien ’n dramatiese toename te danke aan die ontwikkeling van telekommunikasie-infrastruktuur en as gevolg ’n toename in volumes van data en spraak verkeer in die openbaar netwerke.Deur outomaties verwerking van die ruwe toespraak gegee die noodsaaklike hulp verleen aan mense in nood kan word vinniger-up ”, deur te verwys hul oproepe na ’n persoon ingelichte in daardie taal. Tot hierdie effek van ’n toespraak corpus het ontwikkel en die verskillende algoritmes is gemplementeer en getoets op die ruwe telefoon toespraak gegee.Hierdie algoritmes behels die data voorbereiding, seinverwerking, en statistiese analise wat gerig is op onderskei tussen tale.Die statistiese model van Gauss Mengsel Modelle (GGM) was gekies is vir hierdie navorsing as gevolg van hul vermo te verteenwoordig ’n hele taal met’ n enkele stogastiese model wat nodig nie fonetiese tanscription nie. Taal identifiseer vir die Afrikatale gebruik GGM haalbaar is, alhoewel daar enkele paar uitdagings soos behoorlike klassifikasie en akkurate ondersoek na die verhouding van TALE wat moet oorkom moet word.Ander metodes wat gebruik maak van foneties getranskribeerde data nodig om ondersoek te word en getoets word met die nuwe corpus vir die ondersoek te word strenger.
Schuster, Ingmar. "Probabilistic models of natural language semantics." Doctoral thesis, Universitätsbibliothek Leipzig, 2016. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-204503.
Full textDiese Dissertation befasst sich mit der Modellierung der Semantik natürlicher Sprache. Eine Übersicht von Neuronalen Netzwerkmodellen wird gegeben und ein eigener Bayesscher Ansatz wird entwickelt und evaluiert. Da die Leistungsfähigkeit von Standardalgorithmen aus der Monte-Carlo-Familie auf dem entwickelten Model unbefriedigend ist, liegt der Hauptfokus der Arbeit auf neuen adaptiven Algorithmen im Rahmen von Sequential Monte Carlo (SMC). Es wird gezeigt, dass der in der Dissertation entwickelte Gradient Importance Sampling (GRIS) Algorithmus sehr leistungsfähig ist im Vergleich zu vielen Algorithmen des adaptiven Markov Chain Monte Carlo (MCMC), wobei komplexe und hochdimensionale Integrationsprobleme herangezogen werden. Ein weiterer Vorteil im Vergleich mit MCMC ist, dass GRIS einen Schätzer der Modelevidenz liefert. Schließlich wird Sample Inflation eingeführt als Ansatz zur Reduktion von Varianz und schnellerem auffinden von Modi in einer Verteilung, wenn Importance Sampling oder SMC verwendet werden. Sample Inflation ist beweisbar konsistent und es wird empirisch gezeigt, dass seine Anwendung die Konvergenz von Integralschätzern verbessert
Damljanovic, Danica. "Natural language interfaces to conceptual models." Thesis, University of Sheffield, 2011. http://etheses.whiterose.ac.uk/1630/.
Full textDelmestri, Antonella. "Data Driven Models for Language Evolution." Doctoral thesis, Università degli studi di Trento, 2011. https://hdl.handle.net/11572/368357.
Full textDelmestri, Antonella. "Data Driven Models for Language Evolution." Doctoral thesis, University of Trento, 2011. http://eprints-phd.biblio.unitn.it/473/1/PhD-Thesis_Uploaded.pdf.
Full textMiao, 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.
Full textMorganti, Caroline (Caroline Taylor). "Applying natural language models and causal models to project management systems." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/119577.
Full textThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 97-101).
This thesis concerns itself with two problems. First, it examines ways in which to use natural language features in time-varying data in predictive models, specifically applied to the problem of software project maintenance. We attempted to integrate this natural language data into our existing predictive models for project management applications. Second, we began work on creating an easy-to-use, extensible causal modeling framework, a Python package called CEModels. This package allows users to create causal inference models using input data. We tested this framework on project management data as well.
by Caroline Morganti.
M. Eng.
Rodrigeuz-Sanchez, I. "Matrix models of second language vocabulary acquisition." Thesis, Swansea University, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.638702.
Full textLei, Tao Ph D. Massachusetts Institute of Technology. "Interpretable neural models for natural language processing." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/108990.
Full textCataloged from PDF version of thesis.
Includes bibliographical references (pages 109-119).
The success of neural network models often comes at a cost of interpretability. This thesis addresses the problem by providing justifications behind the model's structure and predictions. In the first part of this thesis, we present a class of sequence operations for text processing. The proposed component generalizes from convolution operations and gated aggregations. As justifications, we relate this component to string kernels, i.e. functions measuring the similarity between sequences, and demonstrate how it encodes the efficient kernel computing algorithm into its structure. The proposed model achieves state-of-the-art or competitive results compared to alternative architectures (such as LSTMs and CNNs) across several NLP applications. In the second part, we learn rationales behind the model's prediction by extracting input pieces as supporting evidence. Rationales are tailored to be short and coherent, yet sufficient for making the same prediction. Our approach combines two modular components, generator and encoder, which are trained to operate well together. The generator specifies a distribution over text fragments as candidate rationales and these are passed through the encoder for prediction. Rationales are never given during training. Instead, the model is regularized by the desiderata for rationales. We demonstrate the effectiveness of this learning framework in applications such multi-aspect sentiment analysis. Our method achieves a performance over 90% evaluated against manual annotated rationales.
by Tao Lei.
Ph. D.
Kunz, Jenny. "Neural Language Models with Explicit Coreference Decision." Thesis, Uppsala universitet, Institutionen för lingvistik och filologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-371827.
Full textDavis, Alexandre Guelman. "Subject classification through context-enriched language models." Universidade Federal de Minas Gerais, 2015. http://hdl.handle.net/1843/ESBF-9VKK2Q.
Full textAo longo dos anos, humanos desenvolveram um complexo e intricado sistema de comunicação, com diversas maneiras de transmitir informações, que vão de livros, jornais e televisão até, mais recentemente, mídias sociais. No entanto, recuperar eficientemente e entender mensagens de mídias sociais para a extração de informações úteis é desafiador, especialmente considerando que mensagens mais curtas são mais dependentes do contexto. Usuários muitas vezes assumem que o público de suas mídias sociais está ciente do contexto associado e de eventos do mundo real subjacentes. Isso permite que eles encurtem as mensagens sem prejudicar a efetividade da comunicação. Algoritmos tradicionais de mineração de dados não levam em consideração informações contextuais. Consideramos que explorar o contexto pode levar a uma análise mais completa e precisa das mensagens de mídias sociais. Neste trabalho, portanto, é demonstrado o quão relevantes são as informações contextuais na filtragem de mensagens que são relacionadas a um dado assunto (ou tópico). Também é mostrado que a taxa de recuperação aumenta se o contexto for levado em consideração. Além disso, são propostos métodos para filtrar mensagens relevantes sem utilizar apenas palavras-chave se o contexto for conhecido e datectável. Nesta dissertação, propomos uma nova abordagem para classificação de tópicos em mensagens de mídias sociais que considera tanto informações textuais como extra-textuais (ou contextuais). Essa abordagem propõe e utiliza modelo de linguagem enriquecido com contexto. Técnicas baseadas em conceitos de linguística computacional, mais especificamente na área de Pragmática, são utilizadas. Para avaliar experimentalmente o impacto dessas propostas foram utilizados conjuntos de dados contendo mensagens sobre três importantes esportes americanos (futebol americano, baseball e basquete). Resultados indicam uma melhora de até 50% na recuperação de mensagens sobre estratégias baseadas em texto devido à inclusão de informação contextual.
Pérez-Sancho, Carlos. "Stochastic language models for music information retrieval." Doctoral thesis, Universidad de Alicante, 2009. http://hdl.handle.net/10045/14217.
Full textLabeau, Matthieu. "Neural language models : Dealing with large vocabularies." Thesis, Université Paris-Saclay (ComUE), 2018. http://www.theses.fr/2018SACLS313/document.
Full textThis work investigates practical methods to ease training and improve performances of neural language models with large vocabularies. The main limitation of neural language models is their expensive computational cost: it depends on the size of the vocabulary, with which it grows linearly. Despite several training tricks, the most straightforward way to limit computation time is to limit the vocabulary size, which is not a satisfactory solution for numerous tasks. Most of the existing methods used to train large-vocabulary language models revolve around avoiding the computation of the partition function, ensuring that output scores are normalized into a probability distribution. Here, we focus on sampling-based approaches, including importance sampling and noise contrastive estimation. These methods allow an approximate computation of the partition function. After examining the mechanism of self-normalization in noise-contrastive estimation, we first propose to improve its efficiency with solutions that are adapted to the inner workings of the method and experimentally show that they considerably ease training. Our second contribution is to expand on a generalization of several sampling based objectives as Bregman divergences, in order to experiment with new objectives. We use Beta divergences to derive a set of objectives from which noise contrastive estimation is a particular case. Finally, we aim at improving performances on full vocabulary language models, by augmenting output words representation with subwords. We experiment on a Czech dataset and show that using character-based representations besides word embeddings for output representations gives better results. We also show that reducing the size of the output look-up table improves results even more
Bayer, Ali Orkan. "Semantic Language models with deep neural Networks." Doctoral thesis, Università degli studi di Trento, 2015. https://hdl.handle.net/11572/367784.
Full textBayer, Ali Orkan. "Semantic Language models with deep neural Networks." Doctoral thesis, University of Trento, 2015. http://eprints-phd.biblio.unitn.it/1578/1/bayer_thesis.pdf.
Full textYang, Xi. "Discriminative acoustic and sequence models for GMM based automatic language identification /." View abstract or full-text, 2007. http://library.ust.hk/cgi/db/thesis.pl?ECED%202007%20YANG.
Full textScarcella, Alessandro. "Recurrent neural network language models in the context of under-resourced South African languages." Master's thesis, University of Cape Town, 2018. http://hdl.handle.net/11427/29431.
Full textTakeda, Koichi. "Building Natural Language Processing Applications Using Descriptive Models." 京都大学 (Kyoto University), 2010. http://hdl.handle.net/2433/120372.
Full textHenter, Gustav Eje. "Probabilistic Sequence Models with Speech and Language Applications." Doctoral thesis, KTH, Kommunikationsteori, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-134693.
Full textQC 20131128
ACORNS: Acquisition of Communication and Recognition Skills
LISTA – The Listening Talker
Lou, Bill Pi-ching. "New models of natural language for automated assessment." Thesis, University of Nottingham, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.337661.
Full textGwei, G. M. "New models of natural language for consultative computing." Thesis, University of Nottingham, 1987. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.378986.
Full textMcCandless, Michael Kyle. "Automatic acquisition of language models for speech recognition." Thesis, Massachusetts Institute of Technology, 1994. http://hdl.handle.net/1721.1/36462.
Full textIncludes bibliographical references (leaves 138-141).
by Michael Kyle McCanless.
M.S.
Brorson, Erik. "Classifying Hate Speech using Fine-tuned Language Models." Thesis, Uppsala universitet, Statistiska institutionen, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-352637.
Full textLi, Zhongliang. "Slim Embedding Layers for Recurrent Neural Language Models." Wright State University / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=wright1531950458646138.
Full textShao, Han. "Pretraining Deep Learning Models for Natural Language Understanding." Oberlin College Honors Theses / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=oberlin158955297757398.
Full textTripodi, Rocco <1982>. "Evolutionary game theoretic models for natural language processing." Doctoral thesis, Università Ca' Foscari Venezia, 2015. http://hdl.handle.net/10579/8351.
Full textZausa, Giulio <1998>. "Exploiting Language Models for Vector-Style Images Synthesis." Master's Degree Thesis, Università Ca' Foscari Venezia, 2021. http://hdl.handle.net/10579/19965.
Full textGonzález, Jorge, and Francisco Casacuberta. "Phrase-based finite state models." Universität Potsdam, 2008. http://opus.kobv.de/ubp/volltexte/2008/2720/.
Full textCorreia, Filipe André Sobral. "Model morphisms (MoMo) to enable language independent information models and interoperable business networks." Master's thesis, Faculdade de Ciências e Tecnologia, 2010. http://hdl.handle.net/10362/4782.
Full textWith the event of globalisation, the opportunities for collaboration became more evident with the effect of enlarging business networks. In such conditions, a key for enterprise success is a reliable communication with all the partners. Therefore, organisations have been searching for flexible integrated environments to better manage their services and product life cycle, where their software applications could be easily integrated independently of the platform in use. However, with so many different information models and implementation standards being used, interoperability problems arise. Moreover,organisations are themselves at different technological maturity levels, and the solution that might be good for one, can be too advanced for another, or vice-versa. This dissertation responds to the above needs, proposing a high level meta-model to be used at the entire business network, enabling to abstract individual models from their specificities and increasing language independency and interoperability, while keeping all the enterprise legacy software‟s integrity intact. The strategy presented allows an incremental mapping construction, to achieve a gradual integration. To accomplish this, the author proposes Model Driven Architecture (MDA) based technologies for the development of traceable transformations and execution of automatic Model Morphisms.
Cortez, Marc. "Models, metaphors, and multivalent contextualizations religious language and the nature of contextual theology /." Online full text .pdf document, available to Fuller patrons only, 2004. http://www.tren.com.
Full textWik, Preben. "The Virtual Language Teacher : Models and applications for language learning using embodied conversational agents." Doctoral thesis, KTH, Tal-kommunikation, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-33579.
Full textQC 20110511
Ana, Knežević. "Primena panel modela u identifikovanju faktora uspešnosti poslovanja proizvodnih preduzeća." Phd thesis, Univerzitet u Novom Sadu, Fakultet tehničkih nauka u Novom Sadu, 2015. http://www.cris.uns.ac.rs/record.jsf?recordId=95568&source=NDLTD&language=en.
Full textThe main goal of this research is identifying factors that have an impact onbusiness success of the manufacturing companies, by using themethodology of panel models analysis. Profitability is used as a measure ofbusiness success. Research involves analysis of several internal andexternal factors.Significant influence of several internal (size, financial leverage, efficiency ofassets usage and tangibility of assets) and external factors (inflation, GDPand interest rates) on business success of manufacturing companies hasbeen identified.
Dillehay, Tom D. "Andean Language and Archaeology: A Final Comment." Pontificia Universidad Católica del Perú, 2012. http://repositorio.pucp.edu.pe/index/handle/123456789/113367.
Full textKonstas, Ioannis. "Joint models for concept-to-text generation." Thesis, University of Edinburgh, 2014. http://hdl.handle.net/1842/8926.
Full textAmato, Roberta. "Human collective behavior models: language, cooperation and social conventions." Doctoral thesis, Universitat de Barcelona, 2018. http://hdl.handle.net/10803/565420.
Full textEsta tesis se desarrolla en torno a tres preguntas principales aún abiertas en el contexto del estudio de los comportamientos humanos colectivos: ¿cómo es posible la coexistencia de convenciones (opiniones, idiomas, etc. ) concurrentes?; ¿por qué la cooperación en sistemas reales es más común de lo que se predice?; y ¿cómo una norma inicialmente minoritaria puede suplantar a una mayoría? En el primer trabajo nos centramos en formular un modelo capaz de contemplar la coexistencia de convenciones opuestas como una solución dinámica estable. En el segundo modelo, analizamos la influencia de la dinámica de opinión el primer análisis cuantitativo (el mejor de nuestro conocimiento) del fenómeno de evolución de las normas, es decir, lo que sucede cuando una nueva norma social reemplaza a una norma existente. Resumiendo, los resultados obtenidos en estos trabajos muestran que al modelar los comportamientos humanos colectivos, el hecho de que los individuos participan simultáneamente en diferentes contextos sociales juega un papel importante. Esto implica que los individuos están sujetos tanto a la influencia de diferentes dinámicas sociales como a estructuras de interacciones diferentes, pero no independientes. También hemos demostrado que, en el complejo proceso de cambio colectivo en la adopción de normas, la naturaleza del cambio de normas deja patrones distintos en los datos representados por tres tipos diferentes de transición dinámica. Este último trabajo avanza la comprensión actual de la evolución de las normas, más a menudo limitado a ilustraciones cualitativas (por ejemplo, la observación de que la curva de adopción de la nueva norma sigue un comportamiento ”en forma de S” ).
Clarkson, P. R. "Adaptation of statistical language models for automatic speech recognition." Thesis, University of Cambridge, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.597745.
Full text黃伯光 and Pak-kwong Wong. "Statistical language models for Chinese recognition: speech and character." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1998. http://hub.hku.hk/bib/B31239456.
Full textMukherjee, Niloy 1978. "Spontaneous speech recognition using visual context-aware language models." Thesis, Massachusetts Institute of Technology, 2003. http://hdl.handle.net/1721.1/62380.
Full textIncludes bibliographical references (p. 83-88).
The thesis presents a novel situationally-aware multimodal spoken language system called Fuse that performs speech understanding for visual object selection. An experimental task was created in which people were asked to refer, using speech alone, to objects arranged on a table top. During training, Fuse acquires a grammar and vocabulary from a "show-and-tell" procedure in which visual scenes are paired with verbal descriptions of individual objects. Fuse determines a set of visually salient words and phrases and associates them to a set of visual features. Given a new scene, Fuse uses the acquired knowledge to generate class-based language models conditioned on the objects present in the scene as well as a spatial language model that predicts the occurrences of spatial terms conditioned on target and landmark objects. The speech recognizer in Fuse uses a weighted mixture of these language models to search for more likely interpretations of user speech in context of the current scene. During decoding, the weights are updated using a visual attention model which redistributes attention over objects based on partially decoded utterances. The dynamic situationally-aware language models enable Fuse to jointly infer spoken language utterances underlying speech signals as well as the identities of target objects they refer to. In an evaluation of the system, visual situationally-aware language modeling shows significant , more than 30 %, decrease in speech recognition and understanding error rates. The underlying ideas of situation-aware speech understanding that have been developed in Fuse may may be applied in numerous areas including assistive and mobile human-machine interfaces.
by Niloy Mukherjee.
S.M.
Lin, Chun Hung. "Automatic Question Generation with Pre-trained Masked Language Models." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-289559.
Full textI detta projekt studerar vi uppgiften att generera en fråga från ett givet par av ett textstycke och ett svar med förtränade maskerade språkmodeller. Att ställa frågor är viktigt i utvecklingen av artificiell intelligens eftersom det får en maskin att se intelligent ut när den ställer en rimlig och välkonstruerad fråga. Frågegenerering har också sina applikationer som att formulera frågor för ett läsförståelsetest och att utöka datamängder som kan användas för att träna frågebesvarande program. Vi fokuserar på att använda förtränade maskerade språkmodeller under hela detta projekt. Maskerade språkmodeller är relativt nya i samband med frågegenerering men det har undersökts i maskinöversättningsdomänen. I våra experiment använde vi två träningstekniker och två typer av genereringsordningar. Vi är de första att anta en av dessa träningstekniker för frågegenerering. För utvärdering använde vi n-grambaserad precision-täckning. Vi gjorde även en utvärdering med försökspersoner. Experimentresultaten visade att den bästa metoden var lika bra som LSTM-baserade metoder genom att jämföra resultaten med den tidigare forskningslitteraturen. Dessutom är alla kombinationer av träningsteknikerna och genereringsordningarna acceptabla enligt våra mänskliga utvärderingsresultat. Vi visade också att den nyligen föreslagna tekniken gör det möjligt för oss att kontrollera hur lång den genererade frågan skulle vara.
Borggren, Lukas. "Automatic Categorization of News Articles With Contextualized Language Models." Thesis, Linköpings universitet, Artificiell intelligens och integrerade datorsystem, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-177004.
Full textWong, Pak-kwong. "Statistical language models for Chinese recognition : speech and character /." Hong Kong : University of Hong Kong, 1998. http://sunzi.lib.hku.hk/hkuto/record.jsp?B20158725.
Full textGangireddy, Siva Reddy. "Recurrent neural network language models for automatic speech recognition." Thesis, University of Edinburgh, 2017. http://hdl.handle.net/1842/28990.
Full text