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

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Publications associées (32)

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Prompting Large Language Models to Power Educational Chatbots

Fanny Kim-Lan Lasne, Denis Gillet, Juan Carlos Farah, Sandy Ingram, Basile Maurice Pierre Spaenlehauer

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 ...

Springer2023

A Conceptual Framework for Integrating Conversational Agents in Digital Education

Juan Carlos Farah

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 ...

EPFL2023

4M: Massively Multimodal Masked Modeling

David Mizrahi, Amir Roshan Zamir, Shuqing Teresa Yeo, Oguzhan Fatih Kar, Roman Christian Bachmann

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 ...

Neural Information Processing Systems (Nips)2023

Text Representation Learning for Low Cost Natural Language Understanding

Jan Frederik Jonas Florian Mai

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 ...

EPFL2023

Continual Test-Time Domain Adaptation

Olga Fink

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 ...

IEEE COMPUTER SOC2022

SKILL: Structured Knowledge Infusion for Large Language Models

Martin Jaggi

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 ...

ASSOC COMPUTATIONAL LINGUISTICS-ACL2022

Unsupervised Term Extraction for Highly Technical Domains

Diego Matteo Antognini

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 ...

2022

Textual Explanations and Critiques in Recommendation Systems

Diego Matteo Antognini

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, ...

EPFL2022

Language Transformers for Remote Sensing Visual Question Answering

Devis Tuia, Vincent Alexandre Mendez, Christel Marie Tartini-Chappuis

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 ...

IEEE2022

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

Tanja Christina Käser Jacober, Vinitra Swamy, Thiemo Wambsganss

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 ...

2022

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

Olga Fink

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 ...

ELSEVIER2022

Wind-Topo_Model

Michael Lehning, Jérôme François Sylvain Dujardin

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

Zenodo2022