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Found 12 papers, 5 papers with code
Overview of Factify5WQA: Fact Verification through 5W Question-Answering
Researchers have found that fake news spreads much times faster than real news.
Graph Coarsening via Convolution Matching for Scalable Graph Neural Network Training
Notably, CONVMATCH achieves up to 95% of the prediction performance of GNNs on node classification while trained on graphs summarized down to 1% the size of the original graph.
Overview of Memotion 3: Sentiment and Emotion Analysis of Codemixed Hinglish Memes
In this paper, we present the overview of the Memotion 3 shared task, as part of the DeFactify 2 workshop at AAAI-23.
Findings of Factify 2: Multimodal Fake News Detection
With social media usage growing exponentially in the past few years, fake news has also become extremely prevalent.
Simplifying Distributed Neural Network Training on Massive Graphs: Randomized Partitions Improve Model Aggregation
Backed by our theoretical analysis, instead of maximizing the recovery of cross-instance node dependencies -- which has been considered the key behind closing the performance gap between model aggregation and centralized training -- , our framework leverages randomized assignment of nodes or super-nodes (i. e., collections of original nodes) to partition the training graph such that it improves data uniformity and minimizes the discrepancy of gradient and loss function across instances.
Factify 2: A Multimodal Fake News and Satire News Dataset
In this paper, we provide a multi-modal fact-checking dataset called FACTIFY 2, improving Factify 1 by using new data sources and adding satire articles.
Memotion 3: Dataset on Sentiment and Emotion Analysis of Codemixed Hindi-English Memes
Memes are the new-age conveyance mechanism for humor on social media sites.
A Thousand Words Are Worth More Than a Picture: Natural Language-Centric Outside-Knowledge Visual Question Answering
Outside-knowledge visual question answering (OK-VQA) requires the agent to comprehend the image, make use of relevant knowledge from the entire web, and digest all the information to answer the question.
Transform-Retrieve-Generate: Natural Language-Centric Outside-Knowledge Visual Question Answering
Most previous works address the problem by first fusing the image and question in the multi-modal space, which is inflexible for further fusion with a vast amount of external knowledge.
Ranked #19 on
Visual Question Answering (VQA)
on OK-VQA
Best of Both Worlds: A Hybrid Approach for Multi-Hop Explanation with Declarative Facts
Language-enabled AI systems can answer complex, multi-hop questions to high accuracy, but supporting answers with evidence is a more challenging task which is important for the transparency and trustworthiness to users.
Interactive Teaching for Conversational AI
Current conversational AI systems aim to understand a set of pre-designed requests and execute related actions, which limits them to evolve naturally and adapt based on human interactions.
LRTA: A Transparent Neural-Symbolic Reasoning Framework with Modular Supervision for Visual Question Answering
We show that LRTA makes a step towards truly understanding the question while the state-of-the-art model tends to learn superficial correlations from the training data.
