Search Results for author:
Found 64 papers, 31 papers with code
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.
Beta Distribution Guided Aspect-aware Graph for Aspect Category Sentiment Analysis with Affective Knowledge
To be specific, we first regard each aspect as a pivot to derive aspect-aware words that are highly related to the aspect from external affective commonsense knowledge.
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.
Recent Trends of Multimodal Affective Computing: A Survey from NLP Perspective
The goal of this survey is to explore the current landscape of multimodal affective research, identify development trends, and highlight the similarities and differences across various tasks, offering a comprehensive report on the recent progress in multimodal affective computing from an NLP perspective.
Aspect-Based Sentiment Analysis
Emotion Recognition in Conversation
+2
Weak Reward Model Transforms Generative Models into Robust Causal Event Extraction Systems
The inherent ambiguity of cause and effect boundaries poses a challenge in evaluating causal event extraction tasks.
Encourage or Inhibit Monosemanticity? Revisit Monosemanticity from a Feature Decorrelation Perspective
Consequently, we apply feature correlation as a proxy for monosemanticity and incorporate a feature decorrelation regularizer into the dynamic preference optimization process.
Multi-Layer Ranking with Large Language Models for News Source Recommendation
To seek reliable information sources for news events, we introduce a novel task of expert recommendation, which aims to identify trustworthy sources based on their previously quoted statements.
BoNBoN Alignment for Large Language Models and the Sweetness of Best-of-n Sampling
To answer this, we embed both the best-of-$n$ distribution and the sampling distributions learned by alignment procedures in a common class of tiltings of the base LLM distribution.
PLAYER*: Enhancing LLM-based Multi-Agent Communication and Interaction in Murder Mystery Games
We propose PLAYER*, a novel framework that addresses the limitations of existing agent-based approaches built on Large Language Models (LLMs) in handling complex questions and understanding interpersonal relationships in dynamic environments.
Counterfactual Generation with Identifiability Guarantees
In this work, we tackle the domain-varying dependence between the content and the style variables inherent in the counterfactual generation task.
Addressing Order Sensitivity of In-Context Demonstration Examples in Causal Language Models
This approach utilizes contrastive learning to align representations of in-context examples across different positions and introduces a consistency loss to ensure similar representations for inputs with different permutations.
COPR: Continual Human Preference Learning via Optimal Policy Regularization
Reinforcement Learning from Human Feedback (RLHF) is commonly utilized to improve the alignment of Large Language Models (LLMs) with human preferences.
Towards Unified Task Embeddings Across Multiple Models: Bridging the Gap for Prompt-Based Large Language Models and Beyond
Task embedding, a meta-learning technique that captures task-specific information, has gained popularity, especially in areas such as multi-task learning, model editing, and interpretability.
Mitigating Biases of Large Language Models in Stance Detection with Calibration
This approach enhances the calibration network, facilitating the debiasing and out-of-domain generalization.
Multi-modal Stance Detection: New Datasets and Model
Stance detection is a challenging task that aims to identify public opinion from social media platforms with respect to specific targets.
Mirror: A Multiple-perspective Self-Reflection Method for Knowledge-rich Reasoning
While Large language models (LLMs) have the capability to iteratively reflect on their own outputs, recent studies have observed their struggles with knowledge-rich problems without access to external resources.
Large Language Models Fall Short: Understanding Complex Relationships in Detective Narratives
Existing datasets for narrative understanding often fail to represent the complexity and uncertainty of relationships in real-life social scenarios.
CPPO: Continual Learning for Reinforcement Learning with Human Feedback
The approach of Reinforcement Learning from Human Feedback (RLHF) is widely used for enhancing pre-trained Language Models (LM), enabling them to better align with human preferences.
Reconfigurable Intelligent Surface Deployment for Wideband Millimeter Wave Systems
The performance of wireless communication systems is fundamentally constrained by random and uncontrollable wireless channels.
The Mystery of In-Context Learning: A Comprehensive Survey on Interpretation and Analysis
Understanding in-context learning (ICL) capability that enables large language models (LLMs) to excel in proficiency through demonstration examples is of utmost importance.
Are NLP Models Good at Tracing Thoughts: An Overview of Narrative Understanding
Narrative understanding involves capturing the author's cognitive processes, providing insights into their knowledge, intentions, beliefs, and desires.
A Scalable Framework for Table of Contents Extraction from Complex ESG Annual Reports
Table of contents (ToC) extraction centres on structuring documents in a hierarchical manner.
COPR: Continual Learning Human Preference through Optimal Policy Regularization
The technique of Reinforcement Learning from Human Feedback (RLHF) is a commonly employed method to improve pre-trained Language Models (LM), enhancing their ability to conform to human preferences.
NarrativePlay: Interactive Narrative Understanding
In this paper, we introduce NarrativePlay, a novel system that allows users to role-play a fictional character and interact with other characters in narratives such as novels in an immersive environment.
CUE: An Uncertainty Interpretation Framework for Text Classifiers Built on Pre-Trained Language Models
We then generate text representations by perturbing the latent space which causes fluctuation in predictive uncertainty.
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 through Efficient In-Context Learning
The remarkable performance of pre-trained large language models has revolutionised various natural language processing applications.
Distilling ChatGPT for Explainable Automated Student Answer Assessment
Providing explainable and faithful feedback is crucial for automated student answer assessment.
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.
Heuristics for Vehicle Routing Problem: A Survey and Recent Advances
Vehicle routing is a well-known optimization research topic with significant practical importance.
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.
Concept Algebra for (Score-Based) Text-Controlled Generative Models
This suggests these models have internal representations that encode concepts in a `disentangled' manner.
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.
Integrated Communication and Positioning Design in RIS-empowered OFDM System: a Correlation Dispersion Scheme
The channel frequency responses on pilots (CFROPs) of places of interest are used for online mapping with the offline CFROP database.
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.
Causal Estimation for Text Data with (Apparent) Overlap Violations
To estimate a causal effect from observational data, we need to adjust for confounding aspects of the text that affect both the treatment and outcome -- e. g., the topic or writing level of the text.
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.
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.
A new neighborhood structure for job shop scheduling problems
According to the above finding, this paper proposes a new N8 neighborhood structure considering the movement of critical operations within a critical block and the movement of critical operations outside the critical block.
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-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 #13 on
Emotion Recognition in Conversation
on DailyDialog
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.
Jointly Learning Aspect-Focused and Inter-Aspect Relations with Graph Convolutional Networks for Aspect Sentiment Analysis
Besides, to interactively extract the inter-aspect relations for the specific aspect, an inter-aspect GCN is adopted to model the representations learned by aspect-focused GCN based on the inter-aspect graph which is constructed by the relative dependencies between the aspect words and other aspects.
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).
Transition-based Directed Graph Construction for Emotion-Cause Pair Extraction
Emotion-cause pair extraction aims to extract all potential pairs of emotions and corresponding causes from unannotated emotion text.
Ranked #2 on
Emotion-Cause Pair Extraction
on ECPE-FanSplit
Neural Topic Model with Reinforcement Learning
In recent years, advances in neural variational inference have achieved many successes in text processing.
A Knowledge Regularized Hierarchical Approach for Emotion Cause Analysis
Emotion cause analysis, which aims to identify the reasons behind emotions, is a key topic in sentiment analysis.
Ranked #2 on
Emotion Cause Extraction
on ECE
TDAM: a Topic-Dependent Attention Model for Sentiment Analysis
We propose a topic-dependent attention model for sentiment classification and topic extraction.
A Question Answering Approach for Emotion Cause Extraction
Emotion cause extraction aims to identify the reasons behind a certain emotion expressed in text.
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
Panoptic Studio: A Massively Multiview System for Social Interaction Capture
The core challenges in capturing social interactions are: (1) occlusion is functional and frequent; (2) subtle motion needs to be measured over a space large enough to host a social group; (3) human appearance and configuration variation is immense; and (4) attaching markers to the body may prime the nature of interactions.
Detecting Multiple Replicating Signals using Adaptive Filtering Procedures
Replicability is a fundamental quality of scientific discoveries: we are interested in those signals that are detectable in different laboratories, study populations, across time etc.
Methodology
Panoptic Studio: A Massively Multiview System for Social Motion Capture
We present an approach to capture the 3D structure and motion of a group of people engaged in a social interaction.
