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Which is more faithful,seeing or saying? Multimodal sarcasm detection exploiting contrasting sentiment knowledge

CAAI Transactions on Intelligence Technology 2025年第2期10卷 375-386页

作者： Yutao Chen Shumin Shi Heyan Huang School of Computer Science and Technology Beijing Institute of TechnologyBeijingChina Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications BeijingChina

Using sarcasm on social media platforms to express negative opinions towards a person or object has become increasingly ***,detecting sarcasm in various forms of communication can be difficult due to conflicting *** this paper,we introduce a contrasting sentiment-based model for multimodal sarcasm detection(CS4MSD),which identifies inconsistent emotions by leveraging the CLIP knowledge module to produce sentiment features in both text and ***,five external sentiments are introduced to prompt the model learning sentimental preferences among ***,we highlight the importance of verbal descriptions embedded in illustrations and incorporate additional knowledge-sharing modules to fuse such imagelike *** results demonstrate that our model achieves state-of-the-art performance on the public multimodal sarcasm dataset.

关键词： CLIP image-text classification knowledge fusion multi-modal sarcasm detection

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Analyzing the Potential of Linguistic Features for Sign Spotting: A Look at Approximative Features 2

Analyzing the Potential of Linguistic Features for Sign Spot...

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2nd International Workshop on Automatic Translation for Signed and Spoken languages, AT4SSL 2023

作者： Hollain, Natalie Larson, Martha Roelofsen, Floris Institute for Computing and Information Sciences Radboud University Netherlands Center for Language Studies Institute for Computing and Information Sciences Radboud University Netherlands Institute for Logic Language and Computation University of Amsterdam Netherlands

ISBN: (纸本)9789464857184

Sign language processing is the field of research that aims to recognize, retrieve, and spot signs in videos. Various approaches have been developed, varying in whether they use linguistic features and whether they use landmark detection tools or not. Incorporating linguistics holds promise for improving sign language processing in terms of performance, generalizability, and explainability. This paper focuses on the task of sign spotting and aims to expand on the approximative linguistic features that have been used in previous work, and to understand when linguistic features deliver an improvement over landmark features. We detect landmarks with Mediapipe and extract linguistically relevant features from them, including handshape, orientation, location, and movement. We compare a sign spotting model using linguistic features with a model operating on landmarks directly, finding that the approximate linguistic features tested in this paper capture some aspects of signs better than the landmark features, while they are worse for others. © 2023 2023 VFS Handling Qualities Technical Meeting. All rights reserved.

关键词： Feature extraction

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Generation of Replacement Options in Text Sanitization 24

Generation of Replacement Options in Text Sanitization

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24th Nordic Conference on Computational Linguistics, NoDaLiDa 2023

作者： Olstad, Annika Willoch Papadopoulou, Anthi Lison, Pierre Language Technology Group University of Oslo Oslo Norway Norwegian Computing Center Oslo Norway

ISBN: (纸本)9789916219997

The purpose of text sanitization is to edit text documents to mask text spans that may directly or indirectly reveal personal information. An important problem in text sanitization is to find less specific, yet still informative replacements for each text span to mask. We present an approach to generate possible replacements using a combination of heuristic rules and an ontology derived from Wikidata. Those replacement options are hierarchically structured and cover various types of personal identifiers. Using this approach, we extend a recently released text sanitization dataset with manually selected replacements. The outcome of this data collection shows that the approach is able to suggest appropriate replacement options for most text spans. © 2023 Association for Computational Linguistics.

关键词： Computational linguistics

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Fractality of informativity in 300 years of English scientific writing 7

Fractality of informativity in 300 years of English scientif...

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7th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature, LaTeCH-CLfL 2023

作者： Bizzoni, Yuri Degaetano-Ortlieb, Stefania Center for Humanities Computing Aarhus University Denmark Language Science and Technology Saarland University Germany

ISBN: (纸本)9781959429548

Scientific writing is assumed to have become more informationally dense over time (Halliday, 1988;Biber and Gray, 2016). Given that scientific writing is intended for communication between experts, we hypothesize a tendency towards optimizing language use by striving for a balance between highly informative content and a conventionalized style of writing. We study this by means of fractal analysis, asking whether the degree of informativity has become more persistent with predictable patterns of gradual changes between high vs. low informational content, indicating a trend towards an optimal code for scientific communication. Specifically, surprisal is used to measure informativity and the Hurst exponent is used as a long-term dependence measure for fractality analysis, quantifying the degree of persistence of informativity in scientific texts. © 2023 Association for Computational Linguistics.

关键词： Optimal systems

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Knowledge-enriched joint-learning model for implicit emotion cause extraction

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CAAI Transactions on Intelligence Technology 2023年第1期8卷 118-128页

作者： Chenghao Wu Shumin Shi Jiaxing Hu Heyan Huang School of Computer Science and Technology Beijing Institute of TechnologyBeijingChina Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications BeijingChina

Emotion cause extraction(ECE)task that aims at extracting potential trigger events of certain emotions has attracted extensive attention ***,current work neglects the implicit emotion expressed without any explicit emotional keywords,which appears more frequently in application *** lack of explicit emotion information makes it extremely hard to extract emotion causes only with the local ***,an entire event is usually across multiple clauses,while existing work merely extracts cause events at clause level and cannot effectively capture complete cause event *** address these issues,the events are first redefined at the tuple level and a span-based tuple-level algorithm is proposed to extract events from different *** on it,a corpus for implicit emotion cause extraction that tries to extract causes of implicit emotions is *** authors propose a knowledge-enriched jointlearning model of implicit emotion recognition and implicit emotion cause extraction tasks(KJ-IECE),which leverages commonsense knowledge from ConceptNet and NRC_VAD to better capture connections between emotion and corresponding cause *** on both implicit and explicit emotion cause extraction datasets demonstrate the effectiveness of the proposed model.

关键词： emotion cause extraction external knowledge fusion implicit emotion recognition joint learning

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A Fortiori Case-Based Reasoning: From Theory to Data

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Journal of Artificial Intelligence Research 2024年 81卷 401-441页

作者： van Woerkom, Wijnand Grossi, Davide Prakken, Henry Verheij, Bart Department of Information and Computing Sciences Utrecht University Netherlands Bernoulli Institute for Maths CS and AI University of Groningen Netherlands Institute for Logic Language and Computation Amsterdam Center for Law and Economics University of Amsterdam Netherlands

The widespread application of uninterpretable machine learning systems for sensitive purposes has spurred research into elucidating the decision-making process of these systems. These efforts have their background in many different disciplines, one of which is the field of ai & law. In particular, recent works have observed that machine learning training data can be interpreted as legal cases. Under this interpretation, the formalism developed to study case law, called the theory of precedential constraint, can be used to analyze the way in which machine learning systems draw on training data—or should draw on them—to make decisions. In the present work, we advance the theory underlying these explanation methods, by relating it to order theory and logic. This allows us to write a software implementation of the theory that can be used to compute with the definitions and give automatic proofs of the properties of the model. We use this implementation to evaluate the model on a series of datasets. Through this analysis, we characterize the types of datasets that are more, or less, suitable to be described by the theory. © 2024 The Authors.

关键词： Adversarial machine learning

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Multilingual Synthesis of Depictions through Structured Descriptions of Sign: An Initial Case Study 11

Multilingual Synthesis of Depictions through Structured Desc...

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11th Workshop on the Representation and Processing of Sign languages: Evaluation of Sign language Resources, sign-lang@LREC-COLING 2024

作者： McDonald, John Efthimiou, Eleni Fotinea, Stavroula-Evita Wolfe, Rosalee School of Computing DePaul University ChicagoIL United States Institute for Language and Speech Processing ATHENA Research Center Athens Greece

ISBN: (纸本)9782493814302

Sign language synthesis systems must contend with an enormous variety of possible target languages across the world, and in many locations, such as Europe, the number of sign languages that can be found in a relatively limited geographical area can be surprising. For such a synthesis system to be widely useful, it must not be limited to only one target language. This presents challenges both for the linguistic models and the animation systems that drive these displays. This paper presents a case study for animating discourse in three target languages, French, Greek and German, generated directly from the same base linguistic description. The case study exploits non-lexical constructs in sign, which are more common among sign languages, while providing a first step for synthesizing those aspects that are different. Further, it suggests a possible path forward to exploring whether linguistic structures in one sign language can be exploited in other sign languages, which might be particularly helpful in under-resourced languages. © 2024 ELRA language Resources Association: CC BY-NC 4.0.

关键词： Animation

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Beyond Neural-on-Neural Approaches to Speaker Gender Protection 48

Beyond Neural-on-Neural Approaches to Speaker Gender Protect...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Van Bemmel, Loes Liu, Zhuoran Vaessen, Nik Larson, Martha Institute for Computing and Information Sciences Department of Artificial Intelligence Radboud University Center for Language Studies Nijmegen Netherlands

ISBN: (纸本)9781728163277

Recent research has proposed approaches that modify speech to defend against gender inference attacks. The goal of these protection algorithms is to control the availability of information about a speaker's gender, a privacy-sensitive attribute. Currently, the common practice for developing and testing gender protection algorithms is "neural-on-neural", i.e., perturbations are generated and tested with a neural network. In this paper, we propose to go beyond this practice to strengthen the study of gender protection. First, we demonstrate the importance of testing gender inference attacks that are based on speech features historically developed by speech scientists, alongside the conventionally used neural classifiers. Next, we argue that researchers should use speech features to gain insight into how protective modifications change the speech signal. Finally, we point out that gender-protection algorithms should be compared with novel "vocal adversaries", human-executed voice adaptations, in order to improve interpretability and enable before-the-mic protection. © 2023 IEEE.

关键词： adversarial speech attribute inference gender inference interpretablity neural classifiers

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A Systematic Review of 5G Applications and Impacts in Automotive Manufacturing and Mobility 2

A Systematic Review of 5G Applications and Impacts in Automo...

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2nd International Conference on computing and Data Analytics, ICCDA 2024

作者： Torkzadeh, Moein Hossein Sherimon, Vinu English Language Center University of Technology and Applied Sciences Muscat Oman College of Computing and Information Sciences University of Technology and Applied Sciences Muscat Oman

ISBN: (纸本)9798331542573

The integration of 5G and Internet of Vehicles (IOV) technologies will quicken intelligent transportation system developments, improving urban mobility and safety. This systematic literature review paper summarizes the current research on 5G's uses and impacts in the automotive manufacturing and mobility industries, emphasizing its potential to change transportation scenarios. This systematic literature review examines how 5G technology may impact automobile manufacturing and transportation, with a focus on IOV applications. A comprehensive search across IEEE Xplore, Google Scholar, and ProQuest, yielded 31 relevant publications. Five papers were chosen after applying an exclusion and inclusion criteria to demonstrate how 5G will improve vehicle communication, autonomous driving, and traffic management. The review emphasizes the relevance of Vehicle-to-Everything (V2X) communication technology in enabling real-time information transmission among intelligent connected vehicles, a key to future autonomous driving environmental sensing. © 2024 IEEE.

关键词： 5G mobile communication systems

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Exploring language-Agnostic Speech Representations Using Domain Knowledge for Detecting Alzheimer's Dementia 48

Exploring Language-Agnostic Speech Representations Using Dom...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Shah, Zehra Qi, Shi-Ang Wang, Fei Farrokh, Mahtab Tasnim, Mashrura Stroulia, Eleni Greiner, Russell Plitsis, Manos Katsamanis, Athanasios University of Alberta Department of Computing Science Edmonton Canada Athena Research Center Institute for Language and Speech Processing Greece

ISBN: (纸本)9781728163277

We explore ways to use speech data to screen for indications of Alzheimer's dementia (AD). In particular, we describe our approach to the ICASSP 2023 Signal Processing Grand Challenge, which involves extrapolating from models learned from English speech samples, to Greek speech samples, to determine which subjects have AD. By using acoustic and linguistic features, inspired by clinical research on AD, our top-performing classification model achieves 69% accuracy in distinguishing AD patients from healthy controls, and our regression model attains an RMSE of 4.8 for inferring cognitive testing scores. These outcomes underscore the potential of our explainable model for detecting cognitive decline in AD patients via speech, and its applicability in clinical settings. © 2023 IEEE.

关键词： Alzheimer's dementia Machine Learning Signal Processing Speech

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