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Found 30 papers, 10 papers with code
Measuring Similarity of Opinion-bearing Sentences
For many NLP applications of online reviews, comparison of two opinion-bearing sentences is key.
Evaluation of Review Summaries via Question-Answering
In this paper, we propose to use the question-answering(QA) approach to evaluate summaries of opinions in reviews.
Robustness Analysis of Grover for Machine-Generated News Detection
Grover is a model for both generation and detection of neural fake news.
DUCK: Rumour Detection on Social Media by Modelling User and Comment Propagation Networks
Social media rumours, a form of misinformation, can mislead the public and cause significant economic and social disruption.
A Macro- and Micro-Hierarchical Transfer Learning Framework for Cross-Domain Fake News Detection
Secondly, we propose a macro-hierarchical transfer learning module to generate engagement features based on common users' shared behaviors in different domains for improving effectiveness of knowledge transfer.
Graph2text or Graph2token: A Perspective of Large Language Models for Graph Learning
In this paper, we present a comprehensive review of methodologies for applying LLMs to graphs, termed LLM4graph.
LMOD: A Large Multimodal Ophthalmology Dataset and Benchmark for Large Vision-Language Models
The prevalence of vision-threatening eye diseases is a significant global burden, with many cases remaining undiagnosed or diagnosed too late for effective treatment.
IgnitionInnovators at "Discharge Me!": Chain-of-Thought Instruction Finetuning Large Language Models for Discharge Summaries
This paper presents our proposed approach to the Discharge Me!
Prompted Aspect Key Point Analysis for Quantitative Review Summarization
A majority of KPA studies for reviews adopt supervised learning to extract short sentences as KPs before matching KPs to review comments for quantification of KP prevalence.
FUGNN: Harmonizing Fairness and Utility in Graph Neural Networks
We explore the correlation between sensitive features and spectrum in GNNs, using theoretical analysis to delineate the similarity between original sensitive features and those after convolution under different spectra.
FairGT: A Fairness-aware Graph Transformer
The design of Graph Transformers (GTs) generally neglects considerations for fairness, resulting in biased outcomes against certain sensitive subgroups.
CMA-R:Causal Mediation Analysis for Explaining Rumour Detection
We apply causal mediation analysis to explain the decision-making process of neural models for rumour detection on Twitter.
Ranked #1 on
Rumour Detection
on 1
(using extra training data)
Causal Learning for Trustworthy Recommender Systems: A Survey
However, emerging vulnerabilities in RS have catalyzed a paradigm shift towards Trustworthy RS (TRS).
Bias in Opinion Summarisation from Pre-training to Adaptation: A Case Study in Political Bias
In this study, using political bias as a case study, we first establish a methodology to quantify bias in abstractive models, then trace it from the pre-trained models to the task of summarising social media opinions using different models and adaptation methods.
Harnessing Network Effect for Fake News Mitigation: Selecting Debunkers via Self-Imitation Learning
This study aims to minimize the influence of fake news on social networks by deploying debunkers to propagate true news.
Explainable History Distillation by Marked Temporal Point Process
To efficiently solve \acrshort{ehd}, we rewrite the task into a \gls{01ip} and directly estimate the solution to the program by a model called \acrfull{model}.
Utilising a Large Language Model to Annotate Subject Metadata: A Case Study in an Australian National Research Data Catalogue
To the best of our knowledge, we are introducing, for the first time, an in-context learning method that harnesses large language models for automated subject metadata annotation.
Intensity-free Integral-based Learning of Marked Temporal Point Processes
In the marked temporal point processes (MTPP), a core problem is to parameterize the conditional joint PDF (probability distribution function) $p^*(m, t)$ for inter-event time $t$ and mark $m$, conditioned on the history.
Provably Efficient Bayesian Optimization with Unknown Gaussian Process Hyperparameter Estimation
To address this issue, we propose a new BO method that can sub-linearly converge to the objective function's global optimum even when the true GP hyperparameters are unknown in advance and need to be estimated from the observed data.
Examining Bias in Opinion Summarisation Through the Perspective of Opinion Diversity
We found that BART and ChatGPT can better capture diverse opinions presented in the source documents.
MetaTroll: Few-shot Detection of State-Sponsored Trolls with Transformer Adapters
State-sponsored trolls are the main actors of influence campaigns on social media and automatic troll detection is important to combat misinformation at scale.
Trustworthy Recommender Systems
However, researchers lack a systematic overview and discussion of the literature in this novel and fast developing field of TRSs.
Sequential/Session-based Recommendations: Challenges, Approaches, Applications and Opportunities
In recent years, sequential recommender systems (SRSs) and session-based recommender systems (SBRSs) have emerged as a new paradigm of RSs to capture users' short-term but dynamic preferences for enabling more timely and accurate recommendations.
Does QA-based intermediate training help fine-tuning language models for text classification?
Fine-tuning pre-trained language models for downstream tasks has become a norm for NLP.
Rumour Detection via Zero-shot Cross-lingual Transfer Learning
Most rumour detection models for social media are designed for one specific language (mostly English).
Detecting Singleton Spams in Reviews via Learning Deep Anomalous Temporal Aspect-Sentiment Patterns
Meanwhile, opinion spams spread widely and the detection of spam reviews becomes critically important for ensuring the integrity of the echo system of online reviews.
Less is More: Rejecting Unreliable Reviews for Product Question Answering
In the literature, PQA is formulated as a retrieval problem with the goal to search for the most relevant reviews to answer a given product question.
Sybil-proof Answer Querying Mechanism
We study a question answering problem on a social network, where a requester is seeking an answer from the agents on the network.
Computer Science and Game Theory
Red-faced ROUGE: Examining the Suitability of ROUGE for Opinion Summary Evaluation
Opinion summarisation requires to correctly pair two types of semantic information: (1) aspect or opinion target; and (2) polarity of candidate and reference summaries.
Neural Sparse Topical Coding
Topic models with sparsity enhancement have been proven to be effective at learning discriminative and coherent latent topics of short texts, which is critical to many scientific and engineering applications.
