Search Results for author:
Found 35 papers, 18 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.
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 Text-Controlled Vision Models
In this paper, we introduce a formalization of "what the user intended" in terms of latent concepts implicit to the data generating process that the model was trained on.
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 #8 on
Emotion Recognition in Conversation
on EmoryNLP
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
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
Neural Topic Model with Reinforcement Learning
In recent years, advances in neural variational inference have achieved many successes in text processing.
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.
