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E-book: Natural Language Processing and Chinese Computing: 13th National CCF Conference, NLPCC 2024, Hangzhou, China, November 1-3, 2024, Proceedings, Part I

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The five-volume set LNCS 15359 - 15363 constitutes the refereed proceedings of the 13th National CCF Conference on Natural Language Processing and Chinese Computing, NLPCC 2024, held in Hangzhou, China, during November 2024.





The 161 full papers and 33 evaluation workshop papers included in these proceedings were carefully reviewed and selected from 451 submissions. They deal with the following areas: Fundamentals of NLP; Information Extraction and Knowledge Graph; Information Retrieval, Dialogue Systems, and Question Answering; Large Language Models and Agents; Machine Learning for NLP; Machine Translation and Multilinguality; Multi-modality and Explainability; NLP Applications and Text Mining; Sentiment Analysis, Argumentation Mining, and Social Media; Summarization and Generation. 
Overcoming Rigid and Monotonous: Enhancing Knowledge-grounded
Conversation Generation via Multi-granularity Knowledge.- Learning to
Generate Style-Specific Adapters for Stylized Dialogue Generation.-
Hierarchical Knowledge Aggregation for Personalized Response Generation in
Dialogue Systems.- Multi-hop Reading Comprehension Model Based on Abstract
Meaning Representation and Multi-task Joint Learning.- Leveraging Large
Language Models for QA Dialogue Dataset Construction and Analysis in Public
Services.- MCFC: A Momentum-Driven Clicked Feature Compressed Pre-trained
Language Model for Information Retrieval.- Integrating Syntax Tree and Graph
Neural Network for Conversational Question Answering over Heterogeneous
Sources.- PqE: Zero-Shot Document Expansion for Dense Retrieval with Large
Language Models.- CKF: Conditional Knowledge Fusion Method for CommonSense
Question Answering.- MPPQA: Structure-Aware Extractive Multi-Span Question
Answering for Procedural Documents.- GraphLLM: A General Framework for
Multi-hop Question Answering over Knowledge Graphs using Large Language
Models.- Local or Global Optimization for Dialogue Discourse Parsing.-
Structure and Behavior Dual-Graph Reasoning with Integrated Key-Clue Parsing
for Multi-Party Dialogue Reading Comprehension.- Enhancing Emotional Support
Conversation with Cognitive Chain-of-Thought Reasoning.- A Simple and
Effective Span Interaction Modeling Method for Enhancing Multiple Span
Question Answering.- FacGPT:An Effective and Efficient method for Evaluating
Knowledge-based Visual Question Answering.- PAPER: A Persona-Aware
Chain-of-Thought Learning Framework for Personalized Dialogue Response
Generation.- Towards Building a Robust Knowledge Intensive Question Answering
Model with Large Language Models.- Model-Agnostic Knowledge Distillation
between Heterogeneous Models.- Exploring Multimodal Information Fusion in
Spoken Off-Topic Degree Assessment.- Integrating Hierarchical Key Information
and Semantic Difference Features for Long Text Matching.- CausalAPM:
Generalizable Literal Disentanglement for NLU Debiasing.- W2CL:A Multi-task
Learning Approach to Improve Domain-Specific Sentence Classification through
Word Classification and Contrastive Learning.- Outperforming Larger Models on
Text Classification Through Continued Pre-Training.- Semantic Knowledge
Enhanced and Global Pointer Optimized Method for Medical Nested Entity
Recognition.- CSLAN: A Novel Lexicon Attention Network for Chinese NERS2D:
Enhancing Zero-shot Cross-lingual Event Argument Extraction with Semantic
Knowledge.- Bias-Rectified Multi-way Learning with Data Augmentation for
Implicit Discourse Relation Recognition.- Retrieval-enhanced Template
Generation for Template Extraction.- Chinese Named Entity Recognition Based
on Template and Contrastive Learning.- Enhancing Logical Rules Based on
Self-Distillation for Document-Level Relation ExtractionPrompt-based Joint
Contrastive Learning for Zero-Shot Relation ExtractionLow-Resource Event
Causality Identification With Global Consistency Constraints.- Only One
Relation Possible? Modeling the Ambiguity in Temporal Relation Extraction.-
Empowering LLMs for Long-text Information Extraction in Chinese Legal
Documents.- LLMADR: A Novel Method for Adverse Drug  Reaction Extraction
Based on Style Aligned  Large Language Models Fine-tuning.- Research on Named
Entity Recognition in Ancient Chinese Based on Incremental Pre-training and
Domain Lexicon.- MCKRL: A Multi-Channel based Multi-Graph Knowledge
Representation Learning Model.