Search Results for author:
Found 14 papers, 7 papers with code
Adaptive Data Augmentation for Aspect Sentiment Quad Prediction
Aspect sentiment quad prediction (ASQP) aims to predict the quad sentiment elements for a given sentence, which is a critical task in the field of aspect-based sentiment analysis.
FFT: Towards Harmlessness Evaluation and Analysis for LLMs with Factuality, Fairness, Toxicity
The widespread of generative artificial intelligence has heightened concerns about the potential harms posed by AI-generated texts, primarily stemming from factoid, unfair, and toxic content.
Prompt2Gaussia: Uncertain Prompt-learning for Script Event Prediction
In this paper, we regard public pre-trained language models as knowledge bases and automatically mine the script-related knowledge via prompt-learning.
Dual-Gated Fusion with Prefix-Tuning for Multi-Modal Relation Extraction
Multi-Modal Relation Extraction (MMRE) aims at identifying the relation between two entities in texts that contain visual clues.
Enhancing Multimodal Entity and Relation Extraction with Variational Information Bottleneck
For the first issue, a refinement-regularizer probes the information-bottleneck principle to balance the predictive evidence and noisy information, yielding expressive representations for prediction.
URM4DMU: an user represention model for darknet markets users
Traditional user representation methods mainly rely on modeling the text information of posts and cannot capture the temporal content and the forum interaction of posts.
Event Causality Extraction with Event Argument Correlations
Event Causality Identification (ECI), which aims to detect whether a causality relation exists between two given textual events, is an important task for event causality understanding.
Relation-Guided Few-Shot Relational Triple Extraction
To instantiate this strategy, we further propose a model, RelATE, which builds a dual-level attention to aggregate relationrelevant information to detect the relation occurrence and utilizes the annotated samples of the detected relations to extract the corresponding head/tail entities.
Document-Level Event Extraction via Human-Like Reading Process
Meanwhile, rough reading is explored in a multi-round manner to discover undetected events, thus the multi-events problem is handled.
A Survey on Deep Learning Event Extraction: Approaches and Applications
Numerous methods, datasets, and evaluation metrics have been proposed in the literature, raising the need for a comprehensive and updated survey.
Few-Shot Event Detection with Prototypical Amortized Conditional Random Field
Event detection tends to struggle when it needs to recognize novel event types with a few samples.
Label Enhanced Event Detection with Heterogeneous Graph Attention Networks
A heterogeneous graph attention networks is then introduced to propagate relational message and enrich information interaction.
Inductive Unsupervised Domain Adaptation for Few-Shot Classification via Clustering
We first build a representation extractor to derive features for unlabeled data from the target domain (no test data is necessary) and then group them with a cluster miner.
Edge-Enhanced Graph Convolution Networks for Event Detection with Syntactic Relation
Previous studies on the task have verified the effectiveness of integrating syntactic dependency into graph convolutional networks.
