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Found 33 papers, 10 papers with code
ECNU_ICA at SemEval-2022 Task 10: A Simple and Unified Model for Monolingual and Crosslingual Structured Sentiment Analysis
The task aims to extract the structured sentiment information (e. g., holder, target, expression and sentiment polarity) in a text.
基于多质心异质图学习的社交网络用户建模(User Representation Learning based on Multi-centroid Heterogeneous Graph Neural Networks)
“用户建模已经引起了学术界和工业界的广泛关注。现有的方法大多侧重于将用户间的人际关系融入社区, 而用户生成的内容(如帖子)却没有得到很好的研究。在本文中, 我们通过实际舆情传播相关的分析表明, 在舆情传播过程中对用户属性进行研究的重要作用, 并且提出了用户资料数据的筛选方法。同时, 我们提出了一种通过异构多质心图池为用户捕获更多不同社区特征的建模。我们首先构造了一个由用户和关键字组成的异质图, 并在其上采用了一个异质图神经网络。为了方便用户建模的图表示, 提出了一种多质心图池化机制, 将多质心的集群特征融入到表示学习中。在三个基准数据集上的大量实验表明了该方法的有效性。”
Boosting Conversational Question Answering with Fine-Grained Retrieval-Augmentation and Self-Check
Retrieval-Augmented Generation (RAG) aims to generate more reliable and accurate responses, by augmenting large language models (LLMs) with the external vast and dynamic knowledge.
Enhancing Event Causality Identification with Rationale and Structure-Aware Causal Question Answering
In addition, we generate the rationales to explain why these events have causal relations.
Enhancing Depression-Diagnosis-Oriented Chat with Psychological State Tracking
Depression-diagnosis-oriented chat aims to guide patients in self-expression to collect key symptoms for depression detection.
A Regularization-based Transfer Learning Method for Information Extraction via Instructed Graph Decoder
Information extraction (IE) aims to extract complex structured information from the text.
DiaHalu: A Dialogue-level Hallucination Evaluation Benchmark for Large Language Models
Since large language models (LLMs) achieve significant success in recent years, the hallucination issue remains a challenge, numerous benchmarks are proposed to detect the hallucination.
Learning Intrinsic Dimension via Information Bottleneck for Explainable Aspect-based Sentiment Analysis
To address this, we propose the Information Bottleneck-based Gradient (\texttt{IBG}) explanation framework for ABSA.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+1
Let's Rectify Step by Step: Improving Aspect-based Sentiment Analysis with Diffusion Models
Aspect-Based Sentiment Analysis (ABSA) stands as a crucial task in predicting the sentiment polarity associated with identified aspects within text.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+1
Domain Generalization via Causal Adjustment for Cross-Domain Sentiment Analysis
Domain adaption has been widely adapted for cross-domain sentiment analysis to transfer knowledge from the source domain to the target domain.
MELO: Enhancing Model Editing with Neuron-Indexed Dynamic LoRA
Large language models (LLMs) have shown great success in various Natural Language Processing (NLP) tasks, whist they still need updates after deployment to fix errors or keep pace with the changing knowledge in the world.
Mathematical Language Models: A Survey
In recent years, there has been remarkable progress in leveraging Language Models (LMs), encompassing Pre-trained Language Models (PLMs) and Large-scale Language Models (LLMs), within the domain of mathematics.
Large Language Models at Work in China's Labor Market
We then aggregate occupation exposure to the industry level to obtain industry exposure scores.
AgentSims: An Open-Source Sandbox for Large Language Model Evaluation
With ChatGPT-like large language models (LLM) prevailing in the community, how to evaluate the ability of LLMs is an open question.
EduChat: A Large-Scale Language Model-based Chatbot System for Intelligent Education
Currently, EduChat is available online as an open-source project, with its code, data, and model parameters available on platforms (e. g., GitHub https://github. com/icalk-nlp/EduChat, Hugging Face https://huggingface. co/ecnu-icalk ).
LoGoNet: Towards Accurate 3D Object Detection with Local-to-Global Cross-Modal Fusion
Notably, LoGoNet ranks 1st on Waymo 3D object detection leaderboard and obtains 81. 02 mAPH (L2) detection performance.
Tell Model Where to Attend: Improving Interpretability of Aspect-Based Sentiment Classification via Small Explanation Annotations
Gradient-based explanation methods play an important role in the field of interpreting complex deep neural networks for NLP models.
Causal Intervention-based Prompt Debiasing for Event Argument Extraction
Prompt-based methods have become increasingly popular among information extraction tasks, especially in low-data scenarios.
A Multi-Format Transfer Learning Model for Event Argument Extraction via Variational Information Bottleneck
Event argument extraction (EAE) aims to extract arguments with given roles from texts, which have been widely studied in natural language processing.
Enhancing Event-Level Sentiment Analysis with Structured Arguments
Previous studies about event-level sentiment analysis (SA) usually model the event as a topic, a category or target terms, while the structured arguments (e. g., subject, object, time and location) that have potential effects on the sentiment are not well studied.
A Knowledge-Enhanced Adversarial Model for Cross-lingual Structured Sentiment Analysis
Notably, we propose a Knowledge-Enhanced Adversarial Model (\texttt{KEAM}) with both implicit distributed and explicit structural knowledge to enhance the cross-lingual transfer.
CUP: Curriculum Learning based Prompt Tuning for Implicit Event Argument Extraction
Implicit event argument extraction (EAE) aims to identify arguments that could scatter over the document.
Meta-Weight Graph Neural Network: Push the Limits Beyond Global Homophily
These experiments show the excellent expressive power of MWGNN in dealing with graph data with various distributions.
Reasoning Chain Based Adversarial Attack for Multi-hop Question Answering
The results demonstrate significant performance reduction on both answer and supporting facts prediction, verifying the effectiveness of our reasoning chain based attack method for multi-hop reasoning models and the vulnerability of them.
PoKE: A Prompt-based Knowledge Eliciting Approach for Event Argument Extraction
Eliciting knowledge from pre-trained language models via prompt-based learning has shown great potential in many natural language processing tasks.
ECNUICA at SemEval-2021 Task 11: Rule based Information Extraction Pipeline
The purpose of the task was to extract triples from a paper in the Nature Language Processing field for constructing an Open Research Knowledge Graph.
Magnetoresistance and Kondo effect in the nodal-line semimetal VAs$_2$
We performed calculations of the electronic band structure and the Fermi surface as well as measured the longitudinal resistivity $\rho_{xx}(T, H)$, Hall resistivity $\rho_{xy}(T, H)$, and magnetic susceptibility as a function of temperature and various magnetic fields for VAs$_2$ with a monoclinic crystal structure.
Strongly Correlated Electrons
Automatic Term Name Generation for Gene Ontology: Task and Dataset
Terms contained in Gene Ontology (GO) have been widely used in biology and bio-medicine.
Video Object Segmentation with Adaptive Feature Bank and Uncertain-Region Refinement
We also design a new confidence loss and a fine-grained segmentation module to enhance the segmentation accuracy in uncertain regions.
Ranked #2 on
Semi-Supervised Video Object Segmentation
on Long Video Dataset (3X)
(using extra training data)
Infusing Disease Knowledge into BERT for Health Question Answering, Medical Inference and Disease Name Recognition
Knowledge of a disease includes information of various aspects of the disease, such as signs and symptoms, diagnosis and treatment.
Enhancing Dialogue Symptom Diagnosis with Global Attention and Symptom Graph
In this paper, we first construct a dialogue symptom diagnosis dataset based on an online medical forum with a large amount of dialogues between patients and doctors.
