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Found 24 papers, 13 papers with code
Let's Ask GNN: Empowering Large Language Model for Graph In-Context Learning
Textual Attributed Graphs (TAGs) are crucial for modeling complex real-world systems, yet leveraging large language models (LLMs) for TAGs presents unique challenges due to the gap between sequential text processing and graph-structured data.
Deep Heterogeneous Contrastive Hyper-Graph Learning for In-the-Wild Context-Aware Human Activity Recognition
Human Activity Recognition (HAR) is a challenging, multi-label classification problem as activities may co-occur and sensor signals corresponding to the same activity may vary in different contexts (e. g., different device placements).
Reducing and Exploiting Data Augmentation Noise through Meta Reweighting Contrastive Learning for Text Classification
To boost deep learning models' performance given augmented data/samples in text classification tasks, we propose a novel framework, which leverages both meta learning and contrastive learning techniques as parts of our design for reweighting the augmented samples and refining their feature representations based on their quality.
Heterogeneous Hyper-Graph Neural Networks for Context-aware Human Activity Recognition
In this paper, we argue that context-aware activity visit patterns in realistic in-the-wild data can equivocally be considered as a general graph representation learning task.
SWE2: SubWord Enriched and Significant Word Emphasized Framework for Hate Speech Detection
Our model robustly and significantly performed well under extreme adversarial attack (manipulation of 50% messages), achieving 0. 967 accuracy and 0. 934 macro F1.
An Effective, Robust and Fairness-aware Hate Speech Detection Framework
With the widespread online social networks, hate speeches are spreading faster and causing more damage than ever before.
Wildlife Product Trading in Online Social Networks: A Case Study on Ivory-Related Product Sales Promotion Posts
To counter these environmentally damaging illegal operations, in this research, we focus on wildlife product sales promotion behaviors in online social networks.
Empowering Large Language Models for Textual Data Augmentation
With the capabilities of understanding and executing natural language instructions, Large language models (LLMs) can potentially act as a powerful tool for textual data augmentation.
MEND: Meta dEmonstratioN Distillation for Efficient and Effective In-Context Learning
Large Language models (LLMs) have demonstrated impressive in-context learning (ICL) capabilities, where a LLM makes predictions for a given test input together with a few input-output pairs (demonstrations).
GRENADE: Graph-Centric Language Model for Self-Supervised Representation Learning on Text-Attributed Graphs
Self-supervised representation learning on text-attributed graphs, which aims to create expressive and generalizable representations for various downstream tasks, has received increasing research attention lately.
KEPLET: Knowledge-Enhanced Pretrained Language Model with Topic Entity Awareness
In recent years, Pre-trained Language Models (PLMs) have shown their superiority by pre-training on unstructured text corpus and then fine-tuning on downstream tasks.
Extracting and Visualizing Wildlife Trafficking Events from Wildlife Trafficking Reports
Experts combating wildlife trafficking manually sift through articles about seizures and arrests, which is time consuming and make identifying trends difficult.
Hierarchical Multi-head Attentive Network for Evidence-aware Fake News Detection
Our source code and datasets are released at \texttt{\url{https://github. com/nguyenvo09/EACL2021}}.
HABERTOR: An Efficient and Effective Deep Hatespeech Detector
HABERTOR inherits BERT's architecture, but is different in four aspects: (i) it generates its own vocabularies and is pre-trained from the scratch using the largest scale hatespeech dataset; (ii) it consists of Quaternion-based factorized components, resulting in a much smaller number of parameters, faster training and inferencing, as well as less memory usage; (iii) it uses our proposed multi-source ensemble heads with a pooling layer for separate input sources, to further enhance its effectiveness; and (iv) it uses a regularized adversarial training with our proposed fine-grained and adaptive noise magnitude to enhance its robustness.
Hierarchical Evidence Set Modeling for Automated Fact Extraction and Verification
Automated fact extraction and verification is a challenging task that involves finding relevant evidence sentences from a reliable corpus to verify the truthfulness of a claim.
Where Are the Facts? Searching for Fact-checked Information to Alleviate the Spread of Fake News
The search can directly warn fake news posters and online users (e. g. the posters' followers) about misinformation, discourage them from spreading fake news, and scale up verified content on social media.
Quaternion-Based Self-Attentive Long Short-Term User Preference Encoding for Recommendation
Quaternion space has brought several benefits over the traditional Euclidean space: Quaternions (i) consist of a real and three imaginary components, encouraging richer representations; (ii) utilize Hamilton product which better encodes the inter-latent interactions across multiple Quaternion components; and (iii) result in a model with smaller degrees of freedom and less prone to overfitting.
Attributed Multi-Relational Attention Network for Fact-checking URL Recommendation
To combat fake news, researchers mostly focused on detecting fake news and journalists built and maintained fact-checking sites (e. g., Snopes. com and Politifact. com).
Learning from Fact-checkers: Analysis and Generation of Fact-checking Language
In fighting against fake news, many fact-checking systems comprised of human-based fact-checking sites (e. g., snopes. com and politifact. com) and automatic detection systems have been developed in recent years.
Adversarial Mahalanobis Distance-based Attentive Song Recommender for Automatic Playlist Continuation
Our first approach uses user-playlist-song interactions, and combines Mahalanobis distance scores between (i) a target user and a target song, and (ii) between a target playlist and the target song to account for both the user's preference and the playlist's theme.
Signed Distance-based Deep Memory Recommender
Personalized recommendation algorithms learn a user's preference for an item by measuring a distance/similarity between them.
Regularizing Matrix Factorization with User and Item Embeddings for Recommendation
Following recent successes in exploiting both latent factor and word embedding models in recommendation, we propose a novel Regularized Multi-Embedding (RME) based recommendation model that simultaneously encapsulates the following ideas via decomposition: (1) which items a user likes, (2) which two users co-like the same items, (3) which two items users often co-liked, and (4) which two items users often co-disliked.
The Rise of Guardians: Fact-checking URL Recommendation to Combat Fake News
Despite the existence of these systems, fake news is still wildly shared by online users.
Information Retrieval Social and Information Networks
Identifying On-time Reward Delivery Projects with Estimating Delivery Duration on Kickstarter
In Crowdfunding platforms, people turn their prototype ideas into real products by raising money from the crowd, or invest in someone else's projects.
