Uncertainty and Surprisal Jointly Deliver the Punchline: Exploiting Incongruity-Based Features for Humor Recognition

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The recent rise in both popularity and performance of large language models has garnered considerable interest regarding their applicability to education. Technologies like ChatGPT, which can engage in human-like dialog, have already disrupted educational ...

The presence of conversational agents (or chatbots) in educational contexts has been steadily increasing over the past few years. Recent surveys have shown widespread interest in the use of chatbots in education, both for research and practice. Although th ...

Current machine learning models for vision are often highly specialized and limited to a single modality and task. In contrast, recent large language models exhibit a wide range of capabilities, hinting at a possibility for similarly versatile models in co ...

Natural language processing and other artificial intelligence fields have witnessed impressive progress over the past decade. Although some of this progress is due to algorithmic advances in deep learning, the majority has arguably been enabled by scaling ...

Test-time domain adaptation aims to adapt a source pretrained model to a target domain without using any source data. Existing works mainly consider the case where the target domain is static. However, real-world machine perception systems are running in n ...

Large language models (LLMs) have demonstrated human-level performance on a vast spectrum of natural language tasks. However, it is largely unexplored whether they can better internalize knowledge from a structured data, such as a knowledge graph, or from ...

Term extraction is an information extraction task at the root of knowledge discovery platforms. Developing term extractors that are able to generalize across very diverse and potentially highly technical domains is challenging, as annotations for domains r ...

Artificial intelligence and machine learning algorithms have become ubiquitous. Although they offer a wide range of benefits, their adoption in decision-critical fields is limited by their lack of interpretability, particularly with textual data. Moreover, ...

Remote sensing visual question answering (RSVQA) opens new avenues to promote the use of satellites data, by interfacing satellite image analysis with natural language processing. Capitalizing on the remarkable advances in natural language processing and c ...

Natural Language Processing (NLP) has become increasingly utilized to provide adaptivity in educational applications. However, recent research has highlighted a variety of biases in pre-trained language models. While existing studies investigate bias in di ...

Flood prediction in ungauged catchments is usually conducted by hydrological models that are parameterized based on nearby and similar gauged catchments. As an alternative to this process-based modelling, deep learning (DL) models have demonstrated their a ...

Model and Data: Dujardin, J.; Lehning, M. Wind-Topo_model. EnviDat 2022 ...

Uncertainty and Surprisal Jointly Deliver the Punchline: Exploiting Incongruity-Based Features for Humor Recognition

Chattez avec Graph Search

Prompting Large Language Models to Power Educational Chatbots

A Conceptual Framework for Integrating Conversational Agents in Digital Education

4M: Massively Multimodal Masked Modeling

Text Representation Learning for Low Cost Natural Language Understanding

Continual Test-Time Domain Adaptation

SKILL: Structured Knowledge Infusion for Large Language Models

Unsupervised Term Extraction for Highly Technical Domains

Textual Explanations and Critiques in Recommendation Systems

Language Transformers for Remote Sensing Visual Question Answering

Bias at a Second Glance: A Deep Dive into Bias for German Educational Peer-Review Data Modeling

Generalization of an Encoder-Decoder LSTM model for flood prediction in ungauged catchments

Wind-Topo_Model