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Found 23 papers, 10 papers with code
Findings of the CONSTRAINT 2022 Shared Task on Detecting the Hero, the Villain, and the Victim in Memes
We present the findings of the shared task at the CONSTRAINT 2022 Workshop: Hero, Villain, and Victim: Dissecting harmful memes for Semantic role labeling of entities.
Evaluating and Explaining Natural Language Generation with GenX
Current methods for evaluation of natural language generation models focus on measuring text quality but fail to probe the model creativity, i. e., its ability to generate novel but coherent text sequences not seen in the training corpus.
Foundation Models of Scientific Knowledge for Chemistry: Opportunities, Challenges and Lessons Learned
Foundation models pre-trained on large corpora demonstrate significant gains across many natural language processing tasks and domains e. g., law, healthcare, education, etc.
RAG vs. Long Context: Examining Frontier Large Language Models for Environmental Review Document Comprehension
We compare the performance of the long context LLMs and RAG powered models in handling different types of questions (e. g., problem-solving, divergent).
MemeMQA: Multimodal Question Answering for Memes via Rationale-Based Inferencing
Memes have evolved as a prevalent medium for diverse communication, ranging from humour to propaganda.
Recent Advances in Hate Speech Moderation: Multimodality and the Role of Large Models
In the evolving landscape of online communication, moderating hate speech (HS) presents an intricate challenge, compounded by the multimodal nature of digital content.
Factuality Challenges in the Era of Large Language Models
The emergence of tools based on Large Language Models (LLMs), such as OpenAI's ChatGPT, Microsoft's Bing Chat, and Google's Bard, has garnered immense public attention.
Transparency in Sleep Staging: Deep Learning Method for EEG Sleep Stage Classification with Model Interpretability
A distinctive aspect of this study is the adaptation of GradCam for sleep staging, marking the first instance of an explainable DL model in this domain with alignment of its decision-making with sleep expert's insights.
MEMEX: Detecting Explanatory Evidence for Memes via Knowledge-Enriched Contextualization
In this work, we propose a novel task, MEMEX - given a meme and a related document, the aim is to mine the context that succinctly explains the background of the meme.
Characterizing the Entities in Harmful Memes: Who is the Hero, the Villain, the Victim?
A common problem associated with meme comprehension lies in detecting the entities referenced and characterizing the role of each of these entities.
Quantum-Inspired Tensor Neural Networks for Option Pricing
Recent advances in deep learning have enabled us to address the curse of dimensionality (COD) by solving problems in higher dimensions.
What do you MEME? Generating Explanations for Visual Semantic Role Labelling in Memes
Here, we introduce a novel task - EXCLAIM, generating explanations for visual semantic role labeling in memes.
Domain-aware Self-supervised Pre-training for Label-Efficient Meme Analysis
Existing self-supervised learning strategies are constrained to either a limited set of objectives or generic downstream tasks that predominantly target uni-modal applications.
Quantum-Inspired Tensor Neural Networks for Partial Differential Equations
We demonstrate that TNN provide significant parameter savings while attaining the same accuracy as compared to the classical Dense Neural Network (DNN).
DISARM: Detecting the Victims Targeted by Harmful Memes
Finally, we show that DISARM is interpretable and comparatively more generalizable and that it can reduce the relative error rate for harmful target identification by up to 9 points absolute over several strong multimodal rivals.
Detecting and Understanding Harmful Memes: A Survey
One interesting finding is that many types of harmful memes are not really studied, e. g., such featuring self-harm and extremism, partly due to the lack of suitable datasets.
EXPERT: Public Benchmarks for Dynamic Heterogeneous Academic Graphs
Machine learning models that learn from dynamic graphs face nontrivial challenges in learning and inference as both nodes and edges change over time.
Detecting Harmful Memes and Their Targets
In this work, we propose two novel problem formulations: detecting harmful memes and the social entities that these harmful memes target.
MOMENTA: A Multimodal Framework for Detecting Harmful Memes and Their Targets
We focus on two tasks: (i)detecting harmful memes, and (ii)identifying the social entities they target.
Fighting an Infodemic: COVID-19 Fake News Dataset
This is further exacerbated at the time of a pandemic.
Attentive Recurrent Tensor Model for Community Question Answering
Further, we use the tensor parameters to introduce a 3-way interaction between question, answer and external features in vector space.
On the Benefit of Combining Neural, Statistical and External Features for Fake News Identification
We present a novel idea that combines the neural, statistical and external features to provide an efficient solution to this problem.
Ranked #3 on
Fake News Detection
on FNC-1
