Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 13 of 13 for “"Pretrained Language Models"”.
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Practical neural dialogue management using pretrained language models
… and the need to handle components such as language understanding, state tracking, action selection, and language generation. In this work, we explore the improvements in dialogue management using pretrained language models. We propose three models that incorporate pretrained language …
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Data-Efficient Bilingual Lexicon Induction with Pretrained Language Models
Bilingual dictionaries are essential language resources that play a crucial role in the development of modern multilingual and cross-lingual natural language processing (NLP) systems, particularly for resource-lean languages. Although there are 7,000+ languages spoken worldwide, existing bilingual …
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Geometries of word embeddings
… word embeddings have transformed natural language processing (NLP) applications, recognized for their ability to capture linguistic regularities. Popular examples are word2vec, GloVe, GPT and BERT. Both word2vec and GloVe are static whose word representations are independent of its …
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Probing Language Models for Contextual ScaleUnderstanding
Pretrained language models (LMs) have demonstrated a remarkable ability to emit linguistic and factual knowledge in certain fields. Additionally, they seem to encode relational information about different concepts in a knowledge base. However, since they are trained solely on textual corpora, it is …
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Identification of Atomic Propositions in English Instructions for Flexible Translation to Robot Planning Representations
… robots involves interfacing natural-language instructions into formal representations. This formal representation should contain all the verifiable constituent units (ideally atomic propositions) which are present in the natural language instruction. However, the format and vocabulary …
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Scalable Data Paradigms for Steering General-Purpose Language Models
Pretrained Language Models (LMs) have demonstrated remarkable general-purpose capabilities by encoding vast amounts of knowledge from the internet. However, effectively steering these models to serve diverse downstream applications, such as following instructions, chatting with users, using tools, …
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Investigating Natural Language Interactions in Communities
In this work, we investigate language use in communities. First, we study how authors of scientific textsexplain how their paper relates to another. We propose a new task, relationship explanation generation for scientific texts, by using in-line citation text as a source of evidence. We introduce …
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Improving the Accessibility of Arabic Electronic Theses and Dissertations (ETDs) with Metadata and Classification
… genre of data in the research fields of natural language processing and machine learning. Moreover, much of the related research involved data that is in the English language. Arabic data such as news and tweets have begun to receive some attention in the past decade. However, Arabic ETDs remain …
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Multimodal learning and language models for enhanced knowledge representations
… neural architectures, particularly large-scale language models, has enabled impressive progress across diverse problem domains such as natural language processing, graph representation learning, and tabular data analysis. However, these successes have predominantly relied on abundant, …
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Acquiring and Harnessing Verb Knowledge for Multilingual Natural Language Processing
… in representation learning have enabled natural language processing models to derive non-negligible linguistic information directly from text corpora in an unsupervised fashion. However, this signal is underused in downstream tasks, where they tend to fall back on superficial cues and heuristics …
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Inferring insulin regimen from clinical notes : using natural language processing techniques to extract data from free text records
… outpatient clinical notes. We explore two n-gram models - Logistic Regression and Conditional Random Field and analyze their performance. We also explore models using contextual word representations from the domain specific pretrained language models, character level embeddings and auxillary …
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Machine learning for social event analysis : public perceptions during COVID-19 and collective violence in South Africa
… on any social event. The advancement in natural language processing, computing power, and pretrained language models has given rise to advanced text analysis. Computational analysis of social media complements the sociology framework that theorises human behaviour during a public health crisis or …
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User-guided dynamic topic discovery in large texts
Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2025-05-01