Search Results for author:
Found 85 papers, 27 papers with code
Multi-Modal Sarcasm Detection via Cross-Modal Graph Convolutional Network
Then, the descriptions of the objects are served as a bridge to determine the importance of the association between the objects of image modality and the contextual words of text modality, so as to build a cross-modal graph for each multi-modal instance.
A Large-Scale English Multi-Label Twitter Dataset for Cyberbullying and Online Abuse Detection
In this paper, we introduce a new English Twitter-based dataset for cyberbullying detection and online abuse.
A Multi-label Multi-hop Relation Detection Model based on Relation-aware Sequence Generation
Therefore, in this paper, multi-hop relation detection is considered as a multi-label learning problem.
Beyond Text: Incorporating Metadata and Label Structure for Multi-Label Document Classification using Heterogeneous Graphs
The other is label heterogeneous graph, which is constructed based on both the labels’ hierarchy and their statistical dependencies.
Implicit Sentiment Analysis with Event-centered Text Representation
Implicit sentiment analysis, aiming at detecting the sentiment of a sentence without sentiment words, has become an attractive research topic in recent years.
JointCL: A Joint Contrastive Learning Framework for Zero-Shot Stance Detection
In this paper, we propose a joint contrastive learning (JointCL) framework, which consists of stance contrastive learning and target-aware prototypical graph contrastive learning.
Document-Level Multi-Event Extraction with Event Proxy Nodes and Hausdorff Distance Minimization
By directly minimizing Hausdorff distance, the model is trained towards the global optimum directly, which improves performance and reduces training time.
OverPrompt: Enhancing ChatGPT Capabilities through an Efficient In-Context Learning Approach
The exceptional performance of pre-trained large language models has revolutionised various applications, but their adoption in production environments is hindered by prohibitive costs and inefficiencies, particularly when utilising long prompts.
Distilling ChatGPT for Explainable Automated Student Answer Assessment
Traditional methods of automating student answer assessment through text classification often suffer from issues such as lack of trustworthiness, transparency, and the ability to provide a rationale for the automated assessment process.
BERTTM: Leveraging Contextualized Word Embeddings from Pre-trained Language Models for Neural Topic Modeling
Contextualized word embeddings from pre-trained language models show superiority in the ability of word sense disambiguation and prove to be effective in dealing with OOV words.
Explainable Recommender with Geometric Information Bottleneck
Explainable recommender systems can explain their recommendation decisions, enhancing user trust in the systems.
Cone: Unsupervised Contrastive Opinion Extraction
Contrastive opinion extraction aims to extract a structured summary or key points organised as positive and negative viewpoints towards a common aspect or topic.
Event Knowledge Incorporation with Posterior Regularization for Event-Centric Question Answering
In particular, we define event-related knowledge constraints based on the event trigger annotations in the QA datasets, and subsequently use them to regularize the posterior answer output probabilities from the backbone pre-trained language models used in the QA setting.
NewsQuote: A Dataset Built on Quote Extraction and Attribution for Expert Recommendation in Fact-Checking
To enhance the ability to find credible evidence in news articles, we propose a novel task of expert recommendation, which aims to identify trustworthy experts on a specific news topic.
A User-Centered, Interactive, Human-in-the-Loop Topic Modelling System
Human-in-the-loop topic modelling incorporates users' knowledge into the modelling process, enabling them to refine the model iteratively.
PANACEA: An Automated Misinformation Detection System on COVID-19
In this demo, we introduce a web-based misinformation detection system PANACEA on COVID-19 related claims, which has two modules, fact-checking and rumour detection.
Distinguishability Calibration to In-Context Learning
When using prompt-based learning for text classification, the goal is to use a pre-trained language model (PLM) to predict a missing token in a pre-defined template given an input text, which can be mapped to a class label.
NapSS: Paragraph-level Medical Text Simplification via Narrative Prompting and Sentence-matching Summarization
In this work, we propose a summarize-then-simplify two-stage strategy, which we call NapSS, identifying the relevant content to simplify while ensuring that the original narrative flow is preserved.
Event Temporal Relation Extraction with Bayesian Translational Model
Existing models to extract temporal relations between events lack a principled method to incorporate external knowledge.
Tracking Brand-Associated Polarity-Bearing Topics in User Reviews
Monitoring online customer reviews is important for business organisations to measure customer satisfaction and better manage their reputations.
CAMANet: Class Activation Map Guided Attention Network for Radiology Report Generation
Radiology report generation (RRG) has gained increasing research attention because of its huge potential to mitigate medical resource shortages and aid the process of disease decision making by radiologists.
Event-Centric Question Answering via Contrastive Learning and Invertible Event Transformation
Our proposed model utilizes an invertible transformation matrix to project semantic vectors of events into a common event embedding space, trained with contrastive learning, and thus naturally inject event semantic knowledge into mainstream QA pipelines.
PHEE: A Dataset for Pharmacovigilance Event Extraction from Text
The primary goal of drug safety researchers and regulators is to promptly identify adverse drug reactions.
Once is Enough: A Light-Weight Cross-Attention for Fast Sentence Pair Modeling
Transformer-based models have achieved great success on sentence pair modeling tasks, such as answer selection and natural language inference (NLI).
Addressing Token Uniformity in Transformers via Singular Value Transformation
In this paper, we propose to use the distribution of singular values of outputs of each transformer layer to characterise the phenomenon of token uniformity and empirically illustrate that a less skewed singular value distribution can alleviate the `token uniformity' problem.
PHEMEPlus: Enriching Social Media Rumour Verification with External Evidence
Work on social media rumour verification utilises signals from posts, their propagation and users involved.
Cross-modal Prototype Driven Network for Radiology Report Generation
Radiology report generation (RRG) aims to describe automatically a radiology image with human-like language and could potentially support the work of radiologists, reducing the burden of manual reporting.
RSTGen: Imbuing Fine-Grained Interpretable Control into Long-FormText Generators
In this paper, we study the task of improving the cohesion and coherence of long-form text generated by language models.
Disentangled Learning of Stance and Aspect Topics for Vaccine Attitude Detection in Social Media
Building models to detect vaccine attitudes on social media is challenging because of the composite, often intricate aspects involved, and the limited availability of annotated data.
Natural Language Inference with Self-Attention for Veracity Assessment of Pandemic Claims
The dataset construction includes work on retrieval techniques and similarity measurements to ensure a unique set of claims.
Hierarchical Interpretation of Neural Text Classification
Neural models developed in NLP however often compose word semantics in a hierarchical manner and text classification requires hierarchical modelling to aggregate local information in order to deal with topic and label shifts more effectively.
Evaluating the application of NLP tools in mainstream participatory budgeting processes in Scotland
In recent years participatory budgeting (PB) in Scotland has grown from a handful of community-led processes to a movement supported by local and national government.
Evaluation of Abstractive Summarisation Models with Machine Translation in Deliberative Processes
We present work on summarising deliberative processes for non-English languages.
A mixed-methods ethnographic approach to participatory budgeting in Scotland
Participatory budgeting (PB) is already well established in Scotland in the form of community led grant-making yet has recently transformed from a grass-roots activity to a mainstream process or embedded 'policy instrument'.
Extracting Event Temporal Relations via Hyperbolic Geometry
Recent neural approaches to event temporal relation extraction typically map events to embeddings in the Euclidean space and train a classifier to detect temporal relations between event pairs.
Supervised Contrastive Learning for Multimodal Unreliable News Detection in COVID-19 Pandemic
Rather, previously published news articles on the similar event could be used to assess the credibility of a news report.
Position Bias Mitigation: A Knowledge-Aware Graph Model for Emotion Cause Extraction
To investigate the degree of reliance of existing ECE models on clause relative positions, we propose a novel strategy to generate adversarial examples in which the relative position information is no longer the indicative feature of cause clauses.
Topic-Aware Evidence Reasoning and Stance-Aware Aggregation for Fact Verification
To alleviate the above issues, we propose a novel topic-aware evidence reasoning and stance-aware aggregation model for more accurate fact verification, with the following four key properties: 1) checking topical consistency between the claim and evidence; 2) maintaining topical coherence among multiple pieces of evidence; 3) ensuring semantic similarity between the global topic information and the semantic representation of evidence; 4) aggregating evidence based on their implicit stances to the claim.
Topic-Driven and Knowledge-Aware Transformer for Dialogue Emotion Detection
Emotion detection in dialogues is challenging as it often requires the identification of thematic topics underlying a conversation, the relevant commonsense knowledge, and the intricate transition patterns between the affective states.
Ranked #9 on
Emotion Recognition in Conversation
on EmoryNLP
A Query-Driven Topic Model
Topic modeling is an unsupervised method for revealing the hidden semantic structure of a corpus.
Code Structure Guided Transformer for Source Code Summarization
Code summaries help developers comprehend programs and reduce their time to infer the program functionalities during software maintenance.
Citizen Participation and Machine Learning for a Better Democracy
The development of democratic systems is a crucial task as confirmed by its selection as one of the Millennium Sustainable Development Goals by the United Nations.
Boosting Low-Resource Biomedical QA via Entity-Aware Masking Strategies
Biomedical question-answering (QA) has gained increased attention for its capability to provide users with high-quality information from a vast scientific literature.
Adversarial Learning of Poisson Factorisation Model for Gauging Brand Sentiment in User Reviews
In this paper, we propose the Brand-Topic Model (BTM) which aims to detect brand-associated polarity-bearing topics from product reviews.
BullStop: A Mobile App for Cyberbullying Prevention
Social media has become the new playground for bullies.
CHIME: Cross-passage Hierarchical Memory Network for Generative Review Question Answering
We introduce CHIME, a cross-passage hierarchical memory network for question answering (QA) via text generation.
A Disentangled Adversarial Neural Topic Model for Separating Opinions from Plots in User Reviews
The flexibility of the inference process in Variational Autoencoders (VAEs) has recently led to revising traditional probabilistic topic models giving rise to Neural Topic Models (NTMs).
TRU-NET: A Deep Learning Approach to High Resolution Prediction of Rainfall
Experiments show that our model consistently attains lower RMSE and MAE scores than a DL model prevalent in short term precipitation prediction and improves upon the rainfall predictions of a state-of-the-art dynamical weather model.
A Neural Generative Model for Joint Learning Topics and Topic-Specific Word Embeddings
We propose a novel generative model to explore both local and global context for joint learning topics and topic-specific word embeddings.
Neural Temporal Opinion Modelling for Opinion Prediction on Twitter
Opinion prediction on Twitter is challenging due to the transient nature of tweet content and neighbourhood context.
Neural Topic Modeling with Bidirectional Adversarial Training
Recent years have witnessed a surge of interests of using neural topic models for automatic topic extraction from text, since they avoid the complicated mathematical derivations for model inference as in traditional topic models such as Latent Dirichlet Allocation (LDA).
Ranked #1 on
Text Clustering
on 20 Newsgroups
What Changed Your Mind: The Roles of Dynamic Topics and Discourse in Argumentation Process
In our world with full of uncertainty, debates and argumentation contribute to the progress of science and society.
Topical Phrase Extraction from Clinical Reports by Incorporating both Local and Global Context
Making sense of words often requires to simultaneously examine the surrounding context of a term as well as the global themes characterizing the overall corpus.
Neural Topic Model with Reinforcement Learning
In recent years, advances in neural variational inference have achieved many successes in text processing.
Interpretable Relevant Emotion Ranking with Event-Driven Attention
Unveiling the hidden event information can help to understand how the emotions are evoked and provide explainable results.
Variational Conditional GAN for Fine-grained Controllable Image Generation
In this paper, we propose a novel variational generator framework for conditional GANs to catch semantic details for improving the generation quality and diversity.
Open Event Extraction from Online Text using a Generative Adversarial Network
Experimental results show that our model outperforms the baseline approaches on all the datasets, with more significant improvements observed on the news article dataset where an increase of 15\% is observed in F-measure.
TDAM: a Topic-Dependent Attention Model for Sentiment Analysis
We propose a topic-dependent attention model for sentiment classification and topic extraction.
What You Say and How You Say it: Joint Modeling of Topics and Discourse in Microblog Conversations
This paper presents an unsupervised framework for jointly modeling topic content and discourse behavior in microblog conversations.
ATM:Adversarial-neural Topic Model
The proposed ATM models topics with Dirichlet prior and employs a generator network to capture the semantic patterns among latent topics.
Variational Autoregressive Decoder for Neural Response Generation
Combining the virtues of probability graphic models and neural networks, Conditional Variational Auto-encoder (CVAE) has shown promising performance in applications such as response generation.
An Interpretable Neural Network with Topical Information for Relevant Emotion Ranking
As such, it is crucial to predict and rank multiple relevant emotions by their intensities.
Relevant Emotion Ranking from Text Constrained with Emotion Relationships
As such, emotion detection, to predict multiple emotions associated with a given text, can be cast into a multi-label classification problem.
Neural Storyline Extraction Model for Storyline Generation from News Articles
To tackle this problem, approaches based on probabilistic graphic models jointly model the generations of events and storylines without the use of annotated data.
A Question Answering Approach for Emotion Cause Extraction
Emotion cause extraction aims to identify the reasons behind a certain emotion expressed in text.
Detecting Perspectives in Political Debates
We explore how to detect people{'}s perspectives that occupy a certain proposition.
A Question Answering Approach to Emotion Cause Extraction
Emotion cause extraction aims to identify the reasons behind a certain emotion expressed in text.
Ranked #8 on
Emotion Cause Extraction
on ECE
Event extraction from Twitter using Non-Parametric Bayesian Mixture Model with Word Embeddings
To extract structured representations of newsworthy events from Twitter, unsupervised models typically assume that tweets involving the same named entities and expressed using similar words are likely to belong to the same event.
Adverse Drug Reaction Classification With Deep Neural Networks
On the Twitter dataset, all the NN architectures perform similarly.
Detecting Expressions of Blame or Praise in Text
One of the key aspects of social computing is the ability to attribute responsibility such as blame or praise to social events.
On Stopwords, Filtering and Data Sparsity for Sentiment Analysis of Twitter
In this paper we investigate whether removing stopwords helps or hampers the effectiveness of Twitter sentiment classification methods.
Quantising Opinions for Political Tweets Analysis
There have been increasing interests in recent years in analyzing tweet messages relevant to political events so as to understand public opinions towards certain political issues.
