Search Results for author:
Found 27 papers, 11 papers with code
Coupled Hierarchical Transformer for Stance-Aware Rumor Verification in Social Media Conversations
The prevalent use of social media enables rapid spread of rumors on a massive scale, which leads to the emerging need of automatic rumor verification (RV).
Unified Feature and Instance Based Domain Adaptation for Aspect-Based Sentiment Analysis
The supervised models for aspect-based sentiment analysis (ABSA) rely heavily on labeled data.
Generative Cross-Domain Data Augmentation for Aspect and Opinion Co-Extraction
As a fundamental task in opinion mining, aspect and opinion co-extraction aims to identify the aspect terms and opinion terms in reviews.
Reinforced Counterfactual Data Augmentation for Dual Sentiment Classification
Data augmentation and adversarial perturbation approaches have recently achieved promising results in solving the over-fitting problem in many natural language processing (NLP) tasks including sentiment classification.
A State-independent and Time-evolving Network for Early Rumor Detection in Social Media
To address this limitation, we propose a state-independent and time-evolving Network (STN) for rumor detection based on fine-grained event state detection and segmentation.
End-to-End Emotion-Cause Pair Extraction based on Sliding Window Multi-Label Learning
To tackle these shortcomings, we propose two joint frameworks for ECPE: 1) multi-label learning for the extraction of the cause clauses corresponding to the specified emotion clause (CMLL) and 2) multi-label learning for the extraction of the emotion clauses corresponding to the specified cause clause (EMLL).
Ranked #3 on
Emotion-Cause Pair Extraction
on ECPE
Comparative Opinion Quintuple Extraction from Product Reviews
To address these limitations, in this work we first introduce a new Comparative Opinion Quintuple Extraction (COQE) task, to identify comparative sentences from product reviews and extract all comparative opinion quintuples (Subject, Object, Comparative Aspect, Comparative Opinion, Comparative Preference).
In-Context Learning for Knowledge Base Question Answering for Unmanned Systems based on Large Language Models
However, generating the most appropriate knowledge base query code based on Natural Language Questions (NLQ) poses a significant challenge in KBQA.
A Facial Expression-Aware Multimodal Multi-task Learning Framework for Emotion Recognition in Multi-party Conversations
With the extracted face sequences, we propose a multimodal facial expression-aware emotion recognition model, which leverages the frame-level facial emotion distributions to help improve utterance-level emotion recognition based on multi-task learning.
Ranked #9 on
Emotion Recognition in Conversation
on MELD
Emotion Recognition in Conversation
Facial Expression Recognition (FER)
MEMD-ABSA: A Multi-Element Multi-Domain Dataset for Aspect-Based Sentiment Analysis
Aspect-based sentiment analysis is a long-standing research interest in the field of opinion mining, and in recent years, researchers have gradually shifted their focus from simple ABSA subtasks to end-to-end multi-element ABSA tasks.
UnifiedABSA: A Unified ABSA Framework Based on Multi-task Instruction Tuning
Aspect-Based Sentiment Analysis (ABSA) aims to provide fine-grained aspect-level sentiment information.
Ranked #5 on
Aspect-Based Sentiment Analysis (ABSA)
on ACOS
(using extra training data)
Aspect-Based Sentiment Analysis
Aspect-Category-Opinion-Sentiment Quadruple Extraction
+5
Vision-Language Pre-Training for Multimodal Aspect-Based Sentiment Analysis
Further analysis demonstrates the effectiveness of each pretraining task.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
Multimodal Emotion-Cause Pair Extraction in Conversations
It is also interesting to discover emotions and their causes in conversations.
Aspect-Category-Opinion-Sentiment Quadruple Extraction with Implicit Aspects and Opinions
In this work, we introduce a new task, named Aspect-Category-Opinion-Sentiment (ACOS) Quadruple Extraction, with the goal to extract all aspect-category-opinion-sentiment quadruples in a review sentence and provide full support for aspect-based sentiment analysis with implicit aspects and opinions.
Aspect-Based Sentiment Analysis
Aspect-Category-Opinion-Sentiment Quadruple Extraction
+1
Aspect-Category based Sentiment Analysis with Hierarchical Graph Convolutional Network
In this work, we re-formalize the task as a category-sentiment hierarchy prediction problem, which contains a hierarchy output structure to first identify multiple aspect categories in a piece of text, and then predict the sentiment for each of the identified categories.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+2
Improving Multimodal Named Entity Recognition via Entity Span Detection with Unified Multimodal Transformer
To tackle the first issue, we propose a multimodal interaction module to obtain both image-aware word representations and word-aware visual representations.
Multi-modal Named Entity Recognition
named-entity-recognition
+1
ECPE-2D: Emotion-Cause Pair Extraction based on Joint Two-Dimensional Representation, Interaction and Prediction
In recent years, a new interesting task, called emotion-cause pair extraction (ECPE), has emerged in the area of text emotion analysis.
Ranked #8 on
Emotion-Cause Pair Extraction
on ECPE
Aspect and Opinion Aware Abstractive Review Summarization with Reinforced Hard Typed Decoder
In this paper, we study abstractive review summarization. Observing that review summaries often consist of aspect words, opinion words and context words, we propose a two-stage reinforcement learning approach, which first predicts the output word type from the three types, and then leverages the predicted word type to generate the final word distribution. Experimental results on two Amazon product review datasets demonstrate that our method can consistently outperform several strong baseline approaches based on ROUGE scores.
Global Inference for Aspect and Opinion Terms Co-Extraction Based on Multi-Task Neural Networks
However, most existing methods fail to explicitly consider the syntactic relations among aspect terms and opinion terms, which may lead to the inconsistencies between the model predictions and the syntactic constraints.
Aspect Term Extraction and Sentiment Classification
Multi-Task Learning
+2
Improving Multi-label Emotion Classification via Sentiment Classification with Dual Attention Transfer Network
In this paper, we target at improving the performance of multi-label emotion classification with the help of sentiment classification.
Densely Connected Bidirectional LSTM with Applications to Sentence Classification
Deep neural networks have recently been shown to achieve highly competitive performance in many computer vision tasks due to their abilities of exploring in a much larger hypothesis space.
Modelling Domain Relationships for Transfer Learning on Retrieval-based Question Answering Systems in E-commerce
In this paper, we study transfer learning for the PI and NLI problems, aiming to propose a general framework, which can effectively and efficiently adapt the shared knowledge learned from a resource-rich source domain to a resource- poor target domain.
Leveraging Auxiliary Tasks for Document-Level Cross-Domain Sentiment Classification
In this paper, we study domain adaptation with a state-of-the-art hierarchical neural network for document-level sentiment classification.
Pairwise Relation Classification with Mirror Instances and a Combined Convolutional Neural Network
Relation classification is the task of classifying the semantic relations between entity pairs in text.
