Natural language inference (NLI) is a fundamental and quite challenging task in natural language processing, requiring efficient methods that are able to determine whether given hypotheses derive from given premises. In this paper, we apply explainab...
Natural language inference (NLI) is one of the most important natural language understanding (NLU) tasks. NLI expresses the ability to infer information during spoken or written communication. The NLI task concerns the determination of the entailment...
Two sentences can be related in many different ways. Distinct tasks in natural language processing aim to identify different semantic relations between sentences. We developed several models for natural language inference and semantic textual similar...
Natural Language Inference (NLI) deals with the problem of logical entailment between two sentences. It is evidenced that apart from interpretation of words and syntactic structures, use of external relevant knowledge is also required. This paper inv...
Fall accidents account for more than one-third of fatalities in construction, and fall-related hazards introduce a significant risk of occupational injuries to construction workers. Although numerous efforts have been devoted to preventing falls, mos...
Systems capability is the cognitive ability to plan and execute actions using systems-oriented reasoning. While essential for the design and development of complex systems, it manifests in large organizations primarily through communication and techn...
In the field of Natural Language Inference (NLI), model interpretability remains an urgent and unresolved challenge. Existing interpretability-oriented annotated datasets are highly limited, and manually constructing natural language explanations is...
Inference plays a key role in reading comprehension. However, assessing inference in reading is a complex process that relies on the judgment of trained experts. In this study, we explore objective and automated methods for assessing inference in rea...
Predicting student dropout from universities is an imperative but challenging task. Numerous data-driven approaches that utilize both student demographic information (e.g., gender, nationality, and high school graduation year) and academic informatio...
Ensuring legislative consistency is increasingly difficult as legal systems expand and change more frequently, and the problem is sharper where two language versions are equally binding. This systematic review examines artificial intelligence applied...
Deploying large language models on limited edge hardware remains an open challenge for natural language robot control. This study compares three quantized sub-1.5B models, namely SmolLM2-135M, Gemma3-1B, and TinyLlama-1.1B, for velocity-command gener...
This paper investigates the domain of Natural Language Inference (NLI), with an emphasis on Recognizing Textual Entailment (RTE). We utilize the Stanford Natural Language Inference (SNLI) dataset, a benchmark for RTE tasks, to examine the efficacy of...
Large language models (LLMs) show limited capability in processing low-resource historical languages due to insufficient training data and domain-specific linguistic structures. Korean Literary Sinitic (KLS), the principal written medium of the Joseo...
In recent years, methods that selectively fine-tune or reduce the number of layers in large language models (LLMs) have garnered attention as an efficient alternative to traditional fine-tuning, where all layers are trained. In this study, we revisit...
Integrating causal inference and estimation methods, especially in Natural Language Processsing (NLP), is essential to improve interpretability and robustness in deep learning (DL) models. The objectives are to present the IV-NLP methodology and its...
Hyper-relational extraction aims to identify complex factual structures from unstructured text, where each instance consists of a core triplet and multiple qualified attributes. However, existing hyper-relational datasets often suffer from long-tail...
Scientific research typically follows an iterative cycle where hypotheses are proposed, validated against experimental conclusions, and refined accordingly. While recent advances in large language models (LLMs) have enabled significant progress in au...
It is becoming more challenging for health professionals to keep up to date with current research. To save time, many experts perform evidence syntheses on systematic reviews instead of primary studies. Subsequently, there is a need to update reviews...
Complex question decomposition is an important research topic in the field of natural language processing (NLP). It refers to the decomposition of a compound question containing multiple ontologies and classes into a simple question containing only a...
Large Language Model (LLM) inference engines are becoming critical system infrastructure, yet their increasing architectural complexity makes defects difficult to be diagnosed and repaired. Existing reliability studies predominantly focus on model be...
Recent advances in legal language processing have highlighted limitations in correlation-based artificial intelligence approaches, prompting exploration of Causal Artificial Intelligence (AI) techniques for improved legal reasoning. This systematic r...
Empathetic dialogue systems built upon large language models overwhelmingly adopt a monolithic inference paradigm that processes emotion perception, causal reasoning, memory retrieval, and response planning within a single forward pass without archit...
Using the single premise entailment (SPE) model to accomplish the multi-premise entailment (MPE) task can alleviate the problem that the neural network cannot be effectively trained due to the lack of labeled multi-premise training data. Moreover, th...
The Predictive Rate–Distortion curve quantifies the trade-off between compressing information about the past of a stochastic process and predicting its future accurately. Existing estimation methods for this curve work by clustering finite sequ...
Background: The analytic thinking score (ATS) has been interpreted as a linguistic marker of organized thought. Written language typically shows a higher ATS than spoken language. From the perspective of active inference under the free-energy princip...
Hybrid approaches for conversational NLU commonly rely on cascading strategies, where model selection is performed after inference based on output confidence or uncertainty. Although effective, such post-hoc decision processes introduce redundant com...
Efficient retrieval of mathematical and structural similarities in System Dynamics models remains a significant challenge for traditional lexical systems, which often fail to capture the contextual dependencies of simulation processes. This paper pre...
This paper examines how to apply deep learning techniques to Mongolian bar exam questions. Several approaches that utilize eight different fine-tuned transformer models were demonstrated for recognizing textual inference in Mongolian bar exam questio...
The paper presents a proposed clinical intelligence system, which is called NLP-Bayesian Optimization Clinical Model (NBO-CM), to study large-scale unstructured electronic health record narratives in the MIMIC-IV discharge and radiology note datasets...
With the exponential rise in digital information, the need for an effective multi-document text summarization technique which can provide concise, informative, coherent and reduce redundant information is growing increasingly. The conventional extrac...
Historic expeditions are events that are flavored by exploratory, scientific, military or geographic characteristics. Such events are often documented in literature, journey notes or personal diaries. A typical historic expedition involves multiple s...
Cross-lingual transfer learning using multilingual models has shown promise for improving performance on natural language processing tasks with limited training data. However, translation can introduce superficial patterns that negatively impact mode...
Grammatical inference of context-free grammars using positive and negative language examples is among the most challenging task in modern artificial and natural language technology. Recently, several implementations combining various techniques, usua...
Monotonicity is a prevalent feature in natural language. Notably, determiners such as every, some, and no induce monotonic (upward) or antitonic (downward) entailments. The Monotonicity Calculus is a proof system that formalizes such reasoning in nat...
In the growing field of Natural Language Processing (NLP), transformers have become excessively large, pushing the boundaries of both training and inference compute. Given the size and widespread use of these models, there is now a strong emphasis on...
Cloud-based large language model (LLM) services are increasingly used to process natural-language queries that may contain private or sensitive information. Conventional privacy-preserving approaches, such as cryptographic protection and text sanitiz...
This article addresses the task of inferring elements in the attributes of data. Extracting data related to our interests is a challenging task. Although data on the web can be accessed through free text queries, it is difficult to obtain results tha...
Large-scale pre-trained language models such as BERT have brought much better performance to text classification. However, their large sizes can lead to sometimes prohibitively slow fine-tuning and inference. To alleviate this, various compression me...
A large language model refers to a deep learning model characterized by extensive parameters and pretraining on a large-scale corpus, utilized for processing natural language text and generating high-quality text output. The increasing deployment of...
The task of extracting sentiment from text has been widely studied in the field of natural language processing. However, little work has been conducted specifically on the Arabic language with the Algerian dialect. In this research, we aim to make a...
The internet contains vast amounts of text-based information across various domains, such as commercial documents, medical records, scientific research, engineering tests, and events affecting urban and natural environments. Extracting knowledge from...
In natural language processing (NLP), Transformer is widely used and has reached the state-of-the-art level in numerous NLP tasks such as language modeling, summarization, and classification. Moreover, a variational autoencoder (VAE) is an efficient...
Recognizing textual entailment comprises the task of determining semantic entailment relations between text fragments. A text fragment entails another text fragment if, from the meaning of the former, one can infer the meaning of the latter. If such...
Text information extraction is an important natural language processing (NLP) task, which aims to automatically identify, extract, and represent information from text. In this context, event extraction plays a relevant role, allowing actions, agents,...
A general framework for inference in dynamical systems is described, based on the language of Bayesian probability theory and making use of the maximum entropy principle. Taking the concept of a path as fundamental, the continuity equation and Cauchy...
Ensuring navigational safety is one of the most critical challenges in autonomous maritime navigation research, requiring accurate real-time assessment of collision risks and prompt navigational decisions based on such assessments. Traditional rule-b...
The artificial grammar learning paradigm is a classic method of investigating the influence of universal constraints on shaping learning biases on language acquisition. While this method has been used extensively by linguists to test theoretical clai...
Text classification is a fundamental task in natural language processing (NLP). Deep-learning-based text classification methods usually have two stages: training and inference. However, the training dataset is only used in the training stage. To make...
Recent advances in land system research depend heavily on efficient access to large-scale, multi-source remote sensing spatiotemporal databases. Although Text-to-SQL provides natural language interfaces, the scale and spatial complexity of remote sen...
Incident prioritization is a critical task in enterprise environments, where textual descriptions of service disruptions often contain vague or ambiguous language. Traditional machine learning models, while effective in rigid classification, struggle...
of 6