Search Results for author:
Found 40 papers, 15 papers with code
De-Biased Court's View Generation with Causality
Court{'}s view generation is a novel but essential task for legal AI, aiming at improving the interpretability of judgment prediction results and enabling automatic legal document generation.
Enhance Robustness of Language Models Against Variation Attack through Graph Integration
The widespread use of pre-trained language models (PLMs) in natural language processing (NLP) has greatly improved performance outcomes.
Evolving Knowledge Distillation with Large Language Models and Active Learning
In this paper, we propose EvoKD: Evolving Knowledge Distillation, which leverages the concept of active learning to interactively enhance the process of data generation using large language models, simultaneously improving the task capabilities of small domain model (student model).
Empowering Dual-Level Graph Self-Supervised Pretraining with Motif Discovery
While self-supervised graph pretraining techniques have shown promising results in various domains, their application still experiences challenges of limited topology learning, human knowledge dependency, and incompetent multi-level interactions.
Towards Human-like Perception: Learning Structural Causal Model in Heterogeneous Graph
Heterogeneous graph neural networks have become popular in various domains.
Precedent-Enhanced Legal Judgment Prediction with LLM and Domain-Model Collaboration
Precedents are the previous legal cases with similar facts, which are the basis for the judgment of the subsequent case in national legal systems.
Goal-Oriented Prompt Attack and Safety Evaluation for LLMs
There are several black-box attack methods, such as Prompt Attack, which can change the behaviour of LLMs and induce LLMs to generate unexpected answers with harmful contents.
Community-Based Hierarchical Positive-Unlabeled (PU) Model Fusion for Chronic Disease Prediction
Positive-Unlabeled (PU) Learning is a challenge presented by binary classification problems where there is an abundance of unlabeled data along with a small number of positive data instances, which can be used to address chronic disease screening problem.
PPN: Parallel Pointer-based Network for Key Information Extraction with Complex Layouts
Firstly, the layout of existing datasets is relatively fixed and limited in the number of semantic entity categories, creating a significant gap between these datasets and the complex real-world scenarios.
RexUIE: A Recursive Method with Explicit Schema Instructor for Universal Information Extraction
In addition, we propose RexUIE, which is a Recursive Method with Explicit Schema Instructor for UIE.
Multidimensional Perceptron for Efficient and Explainable Long Text Classification
Because of the inevitable cost and complexity of transformer and pre-trained models, efficiency concerns are raised for long text classification.
Order-Disorder: Imitation Adversarial Attacks for Black-box Neural Ranking Models
In this study, we propose an imitation adversarial attack on black-box neural passage ranking models.
Collaborative Intelligence Orchestration: Inconsistency-Based Fusion of Semi-Supervised Learning and Active Learning
Motivated by the industry practice of labeling data, we propose an innovative Inconsistency-based virtual aDvErsarial Active Learning (IDEAL) algorithm to further investigate SSL-AL's potential superiority and achieve mutual enhancement of AL and SSL, i. e., SSL propagates label information to unlabeled samples and provides smoothed embeddings for AL, while AL excludes samples with inconsistent predictions and considerable uncertainty for SSL.
Dialogue Inspectional Summarization with Factual Inconsistency Awareness
Dialogue summarization has been extensively studied and applied, where the prior works mainly focused on exploring superior model structures to align the input dialogue and the output summary.
A Role-Selected Sharing Network for Joint Machine-Human Chatting Handoff and Service Satisfaction Analysis
Chatbot is increasingly thriving in different domains, however, because of unexpected discourse complexity and training data sparseness, its potential distrust hatches vital apprehension.
A Neural Conversation Generation Model via Equivalent Shared Memory Investigation
Nevertheless, few works utilized the knowledge extracted from similar conversations for utterance generation.
RepSum: Unsupervised Dialogue Summarization based on Replacement Strategy
In the field of dialogue summarization, due to the lack of training data, it is often difficult for supervised summary generation methods to learn vital information from dialogue context with limited data.
Legal Judgment Prediction with Multi-Stage CaseRepresentation Learning in the Real Court Setting
Legal judgment prediction(LJP) is an essential task for legal AI.
Adjacency List Oriented Relational Fact Extraction via Adaptive Multi-task Learning
Relational fact extraction aims to extract semantic triplets from unstructured text.
Ranked #4 on
Relation Extraction
on NYT
Leveraging Online Shopping Behaviors as a Proxy for Personal Lifestyle Choices: New Insights into Chronic Disease Prevention Literacy
Objective: Ubiquitous internet access is reshaping the way we live, but it is accompanied by unprecedented challenges in preventing chronic diseases that are usually planted by long exposure to unhealthy lifestyles.
Semi-Supervised Active Learning for Semi-Supervised Models: Exploit Adversarial Examples With Graph-Based Virtual Labels
Although current mainstream methods begin to combine SSL and AL (SSL-AL) to excavate the diverse expressions of unlabeled samples, these methods' fully supervised task models are still trained only with labeled data.
Unsupervised Summarization for Chat Logs with Topic-Oriented Ranking and Context-Aware Auto-Encoders
Automatic chat summarization can help people quickly grasp important information from numerous chat messages.
Time to Transfer: Predicting and Evaluating Machine-Human Chatting Handoff
Is chatbot able to completely replace the human agent?
Topic-Oriented Spoken Dialogue Summarization for Customer Service with Saliency-Aware Topic Modeling
In a customer service system, dialogue summarization can boost service efficiency by automatically creating summaries for long spoken dialogues in which customers and agents try to address issues about specific topics.
Multi-Turn Dialogue Generation in E-Commerce Platform with the Context of Historical Dialogue
As an important research topic, customer service dialogue generation tends to generate generic seller responses by leveraging current dialogue information.
Toward Recognizing More Entity Types in NER: An Efficient Implementation using Only Entity Lexicons
In this work, we explore the way to quickly adjust an existing named entity recognition (NER) system to make it capable of recognizing entity types not defined in the system.
Cross Copy Network for Dialogue Generation
In the past few years, audiences from different fields witness the achievements of sequence-to-sequence models (e. g., LSTM+attention, Pointer Generator Networks, and Transformer) to enhance dialogue content generation.
Camouflaged Chinese Spam Content Detection with Semi-supervised Generative Active Learning
We propose a Semi-supervIsed GeNerative Active Learning (SIGNAL) model to address the imbalance, efficiency, and text camouflage problems of Chinese text spam detection task.
Aspect Sentiment Classification with Document-level Sentiment Preference Modeling
This justifies the importance of the document-level sentiment preference information to ASC and the effectiveness of our approach capturing such information.
Masking Orchestration: Multi-task Pretraining for Multi-role Dialogue Representation Learning
Multi-role dialogue understanding comprises a wide range of diverse tasks such as question answering, act classification, dialogue summarization etc.
Read Beyond the Lines: Understanding the Implied Textual Meaning via a Skim and Intensive Reading Model
The nonliteral interpretation of a text is hard to be understood by machine models due to its high context-sensitivity and heavy usage of figurative language.
Using Customer Service Dialogues for Satisfaction Analysis with Context-Assisted Multiple Instance Learning
Customers ask questions and customer service staffs answer their questions, which is the basic service model via multi-turn customer service (CS) dialogues on E-commerce platforms.
Human-Like Decision Making: Document-level Aspect Sentiment Classification via Hierarchical Reinforcement Learning
This approach incorporates clause selection and word selection strategies to tackle the data noise problem in the task of DASC.
Detect Camouflaged Spam Content via StoneSkipping: Graph and Text Joint Embedding for Chinese Character Variation Representation
The VFGE can learn both the graph embeddings of the Chinese characters (local) and the latent variation families (global).
Ranked #1 on
Chinese Spam Detection
on SMS
Cross-domain Aspect Category Transfer and Detection via Traceable Heterogeneous Graph Representation Learning
Aspect category detection is an essential task for sentiment analysis and opinion mining.
Aspect Sentiment Classification Towards Question-Answering with Reinforced Bidirectional Attention Network
This paper extends the research to interactive reviews and proposes a new research task, namely Aspect Sentiment Classification towards Question-Answering (ASC-QA), for real-world applications.
Modeling both context- and speaker-sensitive dependence for emotion detection in multi-speaker conversations
On the one hand, our approach represents each utterance and each speaker as a node.
Ranked #56 on
Emotion Recognition in Conversation
on MELD
Sentiment Classification towards Question-Answering with Hierarchical Matching Network
On the basis, we propose a three-stage hierarchical matching network to explore deep sentiment information in a QA text pair.
One vs. Many QA Matching with both Word-level and Sentence-level Attention Network
Question-Answer (QA) matching is a fundamental task in the Natural Language Processing community.
Alibaba at IJCNLP-2017 Task 2: A Boosted Deep System for Dimensional Sentiment Analysis of Chinese Phrases
For word level task our best run achieved MAE 0. 545 (ranked 2nd), PCC 0. 892 (ranked 2nd) in valence prediction and MAE 0. 857 (ranked 1st), PCC 0. 678 (ranked 2nd) in arousal prediction.
