Human Annotated Dialogues Dataset for Natural Conversational Agents by Erinc Merdivan, Deepika Singh, Sten Hanke, Johannes Kropf, Andreas Holzinger, Matthieu Geist

“…Conversational agents are gaining huge popularity in industrial applications such as digital assistants, chatbots, and particularly systems for natural language understanding (NLU). However, a major drawback is the unavailability of a common metric to evaluate the replies against human judgement for conversational agents. …”
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Autonomous GIS: the next-generation AI-powered GIS by Zhenlong Li, Huan Ning

“…By adopting LLM as the reasoning core, we introduce Autonomous GIS as an AI-powered geographic information system (GIS) that leverages the LLM's general abilities in natural language understanding, reasoning, and coding for addressing spatial problems with automatic spatial data collection, analysis, and visualization. …”
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Structural Persistence in Language Models: Priming as a Window into Abstract Language Representations by Arabella Sinclair, Jaap Jumelet, Willem Zuidema, Raquel Fernández

“…We explore how priming can be used to study the potential of these models to learn abstract structural information, which is a prerequisite for good performance on tasks that require natural language understanding skills. We introduce a novel metric and release Prime-LM, a large corpus where we control for various linguistic factors that interact with priming strength. …”
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Multi-Microworld Conversational Agent with RDF Knowledge Graph Integration by Gabriel Boroghina, Dragos Georgian Corlatescu, Mihai Dascalu

“…We ensure high capabilities for natural language understanding by proposing a novel architecture that takes into account RoBERT-contextualized embeddings alongside syntactic features. …”
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A survey on Relation Extraction by Kartik Detroja, C.K. Bhensdadia, Brijesh S. Bhatt

“…Many NLP applications can benefit from relational information derived from natural language, including Structured Search, Knowledge Base (KB) population, Information Retrieval, Question-Answering, Language Understanding, Ontology Learning, etc. This survey covers (1) basic concepts of Relation Extraction; (2) various Relation Extraction methodologies; (3) Deep Learning techniques for Relation Extraction; and (4) different datasets that can be used to evaluate the RE system.…”
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Particularities of rhetorical culture in Slovakia. Exploring the presidential campaign in 2014

“…Gillet saying that the human thinking is non-formal and, in the same time, sensitive to the context of real word, we admit the linguistic claim, which underlines the conditionality of language understanding by understanding of any discourse happening (including people acting in different situations, reproducing and developing themselves as socio-cultural creatures) – cf. …”
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Gender and culture bias in letters of recommendation for computer science and data science masters programs by Yijun Zhao, Zhengxin Qi, John Grossi, Gary M. Weiss

“…One set of features, which measure the underlying sentiment, tone, and emotions associated with each LOR, is automatically generated using IBM Watson’s Natural Language Understanding (NLU) service. The second set of features is measured manually by our research team and quantifies the relevance, specificity, and positivity of each LOR. …”
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Understanding language through visual imagination by Mao, Cheahuychou.

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Next-generation consumer innovation search: Identifying early-stage need-solution pairs on the web by von Hippel, Eric A, Kaulartz, Sandro

“…Today, technical advances in machine learning techniques for natural language understanding, such as semantic word space models and semantic network analytics, have made it practical to capture descriptions of early-stage, need-solution pairs mentioned anywhere in the open, textual content of the Internet. …”
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Compositional Robot Learning for Generalizable Interactions by Kuo, Yen-Ling

“…This thesis presents several learning-based approaches that leverage compositionally to enable generalization in various reasoning skills such as language understanding and social interactions. First, we show how we can derive networks directly from compositional linguistic structures to enable robots to follow novel commands and act rationally in new scenarios. …”
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Flexible Energy-Aware Image and Transformer Processors for Edge Computing by Ji, Alex

“…Lastly, an energy-adaptive accelerator for natural language understanding based on lightweight Transformer models is presented. …”
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Nonliteral understanding of number words by Bergen, Leon, Kao, Justine T., Wu, Jean Y., Goodman, Noah D.

“…This modeling framework provides a unified approach to nonliteral language understanding more generally.…”
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Sign language recognition using deep learning through LSTM and CNN by Kiran, Pandian, Mohd Azraai, Mohd Razman, Ismail, Mohd Khairuddin, Muhammad Amirul, Abdullah, Ahmad Fakhri, Ab. Nasir, Wan Hasbullah, Mohd Isa

“…This study presents the application of using deep learning to detect, recognize and translate sign language. Understanding sign language is crucial for communication between the deaf and mute people and the general society. …”
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Web-based learning through mixed-initiative interactions: Design and implementation by Nantha Kumar Subramaniam

“…Here, the system supports the learning through a problem solving activity by demanding active learning behaviour from the learner with minimal natural language understanding by the agent and embodies the application-dependent aspects of the discourse. …”
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Intent Detection and Slot Filling with Capsule Net Architectures for a Romanian Home Assistant by Anda Stoica, Tibor Kadar, Camelia Lemnaru, Rodica Potolea, Mihaela Dînşoreanu

“…This paper explores the problem of Natural Language Understanding (NLU) applied to a Romanian home assistant. …”
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An Approach for Curating Collections of Historical Documents with the Use of Topic Detection Technologies by Medina Andresel, Sergiu Gordea, Srdjan Stevanetic, Mina Schütz

“…Technological advances in natural language understanding and in data management have led to the automation of document categorization and via automatic topic detection. …”
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A Review of Current Trends, Techniques, and Challenges in Large Language Models (LLMs) by Rajvardhan Patil, Venkat Gudivada

“…Large language models (LLMs) have achieved SOTA performances on natural language understanding (NLU) and natural language generation (NLG) tasks by learning language representation in self-supervised ways. …”
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Conversational AI over Military Scenarios Using Intent Detection and Response Generation by Hsiu-Min Chuang, Ding-Wei Cheng

“…This paper proposes a system architecture for conversational military agents based on natural language understanding (NLU) and natural language generation (NLG). …”
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Improving Intent Classification Using Unlabeled Data from Large Corpora by Gabriel Bercaru, Ciprian-Octavian Truică, Costin-Gabriel Chiru, Traian Rebedea

“…Intent classification is a central component of a Natural Language Understanding (NLU) pipeline for conversational agents. …”
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Medical QA Oriented Multi-Task Learning Model for Question Intent Classification and Named Entity Recognition by Turdi Tohti, Mamatjan Abdurxit, Askar Hamdulla

“…Intent classification and named entity recognition of medical questions are two key subtasks of the natural language understanding module in the question answering system. …”
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