Natural Language Processing and Chinese Computing

Name: Natural Language Processing and Chinese Computing | 11th CCF International Conference, NLPCC 2022, Guilin, China, September 24-25, 2022, Proceedings, Part II
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11th CCF International Conference, NLPCC 2022, Guilin, China, September 24-25, 2022, Proceedings, Part II

Wei Lu Shujian Huang Yu Hong Xiabing Zhou(Editor)

Springer (Publisher)

Published on 24. September 2022

XXXI, 363 pages

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978-3-031-17189-5 (ISBN)

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Content

Intro
Preface
Organization
Contents - Part II
Contents - Part I
Question Answering (Poster)
Faster and Better Grammar-Based Text-to-SQL Parsing via Clause-Level Parallel Decoding and Alignment Loss
1 Introduction
2 Related Works
3 Our Proposed Model
3.1 Grammar-Based Text-to-SQL Parsing
3.2 Clause-Level Parallel Decoding
3.3 Clause-Level Alignment Loss
4 Experiments
4.1 Experimental Setup
4.2 Results
4.3 Analysis
5 Conclusions
References
Two-Stage Query Graph Selection for Knowledge Base Question Answering
1 Introduction
2 Our Approach
2.1 Query Graph Generation
2.2 Two-Stage Query Graph Selection
3 Experiments
3.1 Experimental Setup
3.2 Main Results
3.3 Discussion and Analysis
4 Related Work
5 Conclusions
References
Plug-and-Play Module for Commonsense Reasoning in Machine Reading Comprehension
1 Introduction
2 Methodology
2.1 Task Formulation
2.2 Proposed Module: PIECER
2.3 Plugging PIECER into MRC Models
3 Experiments
3.1 Datasets
3.2 Base Models
3.3 Experimental Settings
3.4 Main Results
3.5 Analysis and Discussions
4 Related Work
5 Conclusion
References
Social Media and Sentiment Analysis (Poster)
FuDFEND: Fuzzy-Domain for Multi-domain Fake News Detection
1 Introduction
2 Related Work
2.1 Fake News Detection Methods
2.2 Multi-domain Rumor Task
3 FuDFEND: Fuzzy-Domain Fake News Detection Model
3.1 Membership Function
3.2 Feature Extraction
3.3 Domain Gate
3.4 Fake News Prediction and Loss Function
4 Experiment
4.1 Dataset
4.2 Experiment Setting
4.3 Train Membership Function and FuDFEND
4.4 Experiment on Weibo21
4.5 Experiment on Thu Dataset
5 Conclusion
6 Future Work
References
NLP Applications and Text Mining (Poster)
Continuous Prompt Enhanced Biomedical Entity Normalization
1 Introduction
2 Related Work
2.1 Biomedical Entity Normalization
2.2 Prompt Learning and Contrastive Loss
3 Our Method
3.1 Prompt Enhanced Scoring Mechanism
3.2 Contrastive Loss Enhanced Training Mechanism
4 Experiments and Analysis
4.1 Dataset and Evaluation
4.2 Data Preprocessing
4.3 Experiment Setting
4.4 Overall Performance
4.5 Ablation Study
5 Conclusion
References
Bidirectional Multi-channel Semantic Interaction Model of Labels and Texts for Text Classification
1 Introduction
2 Model
2.1 Preliminaries
2.2 Bidirectional Multi-channel Semantic Interaction Model
3 Experiments
3.1 Experimental Settings
3.2 Results and Analysis
3.3 Ablation Test
4 Conclusions
References
Exploiting Dynamic and Fine-grained Semantic Scope for Extreme Multi-label Text Classification
1 Introduction
2 Related Work
3 Methodology
3.1 Notation
3.2 TReaderXML
4 Experiments
4.1 Datasets and Preprocessing
4.2 Baselines
4.3 Evaluation Metrics
4.4 Ablation Study
4.5 Performance on Tail Labels
5 Conclusions
References
MGEDR: A Molecular Graph Encoder for Drug Recommendation
1 Introduction
2 Related Works
2.1 Drug Recommendation
2.2 Molecular Graph Representation
3 Problem Formulation
4 The MGEDR Model
4.1 Patient Encoder
4.2 Medicine Encoder
4.3 Functional Groups Encoder
4.4 Medicine Representation
4.5 Optimization
5 Experiments
5.1 Dataset and Metrics
5.2 Results
5.3 Ablations
6 Conclusion
References
Student Workshop (Poster)
Semi-supervised Protein-Protein Interactions Extraction Method Based on Label Propagation and Sentence Embedding
1 Introduction
2 Related Work
3 Methods
3.1 Problem Formulation
3.2 Overall Workflow
3.3 Label Propagation
3.4 Sentence Embedding
3.5 CNN Classifier
4 Results
4.1 Datasets and Preprocessing
4.2 Experimental Results
4.3 Hyperparameter Analysis
5 Conclusion
References
Construction and Application of a Large-Scale Chinese Abstractness Lexicon Based on Word Similarity
1 Introduction
2 Data and Method
2.1 Data
2.2 Method
3 Experiment
4 Construction and Evaluation
5 Application
5.1 Cross-Language Comparison
5.2 Chinese Text Readability Auto-evaluation
6 Conclusion
References
Stepwise Masking: A Masking Strategy Based on Stepwise Regression for Pre-training
1 Introduction
2 Methodology
2.1 Three-Stage Framework
2.2 Stepwise Masking
3 Experiments
3.1 Datasets
3.2 Experimental Settings
3.3 Main Results
3.4 Effectiveness of Stepwise Masking
3.5 Effect of Dynamic in Stepwise Masking
3.6 Case Study
4 Conclusion and Future Work
References
Evaluation Workshop (Poster)
Context Enhanced and Data Augmented W2NER System for Named Entity Recognition
1 Introduction
2 Related Work
3 The Proposed Approach
3.1 Task Definition
3.2 Model Structure
3.3 Data Augmentation
3.4 Result Ensemble
4 Experiments
4.1 Dataset and Metric
4.2 Experiment Settings
4.3 Baselines
4.4 Results and Analysis
5 Conclusion
References
Multi-task Hierarchical Cross-Attention Network for Multi-label Text Classification
1 Introduction
2 Related Work
2.1 Hierarchical Multi-label Text Classification
2.2 Representation of Scientific Literature
3 Methodology
3.1 Representation Layer
3.2 Hierarchical Cross-Attention Recursive Layer
3.3 Hierarchical Prediction Layer
3.4 Rebalanced Loss Function
4 Experiment
4.1 Dataset and Evaluation
4.2 Experimental Settings
4.3 Results and Discussions
4.4 Module Analysis
5 Conclusion
References
An Interactive Fusion Model for Hierarchical Multi-label Text Classification
1 Introduction
2 Related Work
3 Task Definition
4 Method
4.1 Shared Encoder Module
4.2 Task-Specific Module
4.3 Training and Inference
5 Experiment
6 Conclusion
References
Scene-Aware Prompt for Multi-modal Dialogue Understanding and Generation
1 Introduction
2 Task Introduction
2.1 Problem Definition
2.2 Evaluation Metric
2.3 Dateset
3 Main Methods
3.1 Multi-tasking Multi-modal Dialogue Understanding
3.2 Scene-Aware Prompt Multi-modal Dialogue Generation
3.3 Training and Inference
4 Experiments
4.1 Experimental Setup
4.2 Main Results
4.3 Ablation Study
4.4 Online Results
5 Conclusion
References
BIT-WOW at NLPCC-2022 Task5 Track1: Hierarchical Multi-label Classification via Label-Aware Graph Convolutional Network
1 Introduction
2 Approach
2.1 Context-Aware Label Embedding
2.2 Graph-Based Hierarchical Label Modeling
2.3 Curriculum Learning Strategy
2.4 Ensemble Learning and Post Editing
3 Experiments
3.1 Dataset and Experiment Settings
3.2 Main Results
3.3 Analysis
4 Related Work
5 Conclusion
References
CDAIL-BIAS MEASURER: A Model Ensemble Approach for Dialogue Social Bias Measurement
1 Introduction
2 Related Work
2.1 Shared Tasks
2.2 Solution Models
3 Dataset
4 Method
4.1 Models Selection
4.2 Fine-Tuning Strategies
4.3 Ensembling Strategy
5 Result
5.1 Preliminary Screening
5.2 Model Ensemble
5.3 Ensemble Size Effect
5.4 Discussion
6 Conclusion
References
A Pre-trained Language Model for Medical Question Answering Based on Domain Adaption
1 Introduction
2 Related Work
2.1 Encoder-Based
2.2 Decoder-Based
2.3 Encoder-Decoder-Based
3 Description of the Competition
3.1 Evaluation Metrics
3.2 Datasets
4 Solution
4.1 Model Introduction
4.2 Strategy
4.3 Model Optimization
4.4 Model Evaluation
5 Conclusion
References
Enhancing Entity Linking with Contextualized Entity Embeddings
1 Introduction
2 Related Work
3 Methodology
3.1 Dual Encoder
3.2 LUKE-Based Cross-Encoder
4 Experiments
4.1 Data
4.2 Candidate Retrieval
4.3 Candidate Reranking
5 Conclusion
References
A Fine-Grained Social Bias Measurement Framework for Open-Domain Dialogue Systems
1 Introduction
2 Related Work
2.1 Fine Grained Dialogue Social Bias Measurement
2.2 Application of Contrastive Learning in NLP Tasks
2.3 Application of Prompt Learning in NLP Tasks
3 Fine-Grain Dialogue Social Bias Measurement Framework
3.1 General Representation Module
3.2 Two-Stage Prompt Learning Module
3.3 Contrastive Learning Module
4 Experiment
4.1 Dataset
4.2 Experimental Setup
4.3 Results and Analysis
5 Conclusion
References
Dialogue Topic Extraction as Sentence Sequence Labeling
1 Introduction
2 Related Work
2.1 Dialogue Topic Information
2.2 Sequence Labeling
3 Methodology
3.1 Task Definition
3.2 Topic Extraction Model
3.3 Ensemble Model
4 Experiments
4.1 Dataset
4.2 Results and Analysis
5 Conclusion
References
Knowledge Enhanced Pre-trained Language Model for Product Summarization
1 Introduction
2 Related Work
2.1 Encoder-Decoder Transformer
2.2 Decoder-Only Transformer
3 Description of the Competition
4 Dataset Introduction
4.1 Textual Data
4.2 Image Data
5 Model Solution
5.1 Model Introduction
5.2 Model Training
6 Model Evaluation
7 Conclusion
References
Augmented Topic-Specific Summarization for Domain Dialogue Text
1 Introduction
2 Related Work
3 Dataset
3.1 Data Preprocessing
3.2 Constrained Search of Training Data
4 System Overview
4.1 Sub-summary Generation Model
4.2 Ensemble Topic Detection
4.3 Data Augmentation
5 Experiments
6 Analysis
6.1 The Effect of Different Data Augmentation Methods
6.2 The Effect of Classification
7 Conclusion
References
DAMO-NLP at NLPCC-2022 Task 2: Knowledge Enhanced Robust NER for Speech Entity Linking
1 Introduction
2 Related Work
3 Methods
3.1 Candidate Entity Retriever
3.2 Knowledge Enhanced NER
3.3 Linking with Filtering
3.4 Ensemble Methods
4 Experiments
4.1 Data Analysis and Experimental Settings
4.2 Main Results
4.3 Ablation Study
5 Conclusions
References
Overview of the NLPCC2022 Shared Task on Speech Entity Linking
1 Introduction
2 Related Work
3 Task Description
3.1 Track 1: Entity Recognition and Disambiguation
3.2 Track 2: Entity Disambiguation-Only
4 Dataset Description
5 Result
5.1 Evaluation Metrics
5.2 Participants and Results
5.3 Analysis
6 Conclusion
References
Overview of the NLPCC 2022 Shared Task on Multimodal Product Summarization
1 Introduction
2 Task Definition
3 Dataset
4 Evaluation
5 Participants
6 Results
6.1 Automatic Evaluation Results
6.2 Human Evaluation Results
7 Conclusion
A An Example from the Dataset
References
A Multi-task Learning Model for Fine-Grain Dialogue Social Bias Measurement
1 Introduction
2 Related Work
3 Proposed System
3.1 Problem Definition
3.2 Text Representation
3.3 Auxiliary Tasks
3.4 Main Task
3.5 Model Fusion
3.6 Adversarial Training
3.7 Training Loss
4 Experiments and Details
4.1 Dataset
4.2 Implement Details
4.3 Results and Analysis
5 Conclusion
References
Overview of NLPCC2022 Shared Task 5 Track 1: Multi-label Classification for Scientific Literature
1 Introduction
2 Related Work
3 Data
4 Methods from Participants
5 Results
6 Conclusion
References
Overview of the NLPCC 2022 Shared Task: Multi-modal Dialogue Understanding and Generation
1 Introduction
2 Task Description
2.1 Dialogue Scene Identification
2.2 Dialogue Session Identification
2.3 Dialogue Response Generation
3 Dataset Description
4 Results
4.1 Evaluation Metrics
4.2 Evaluation Results
5 Conclusion
References
Overview of NLPCC2022 Shared Task 5 Track 2: Named Entity Recognition
1 Introduction
2 Task Definition
3 Dataset Preparation
4 Evaluation Metric
5 Approaches and Results
6 Conclusion
References
Overview of NLPCC 2022 Shared Task 7: Fine-Grained Dialogue Social Bias Measurement
1 Introduction
2 Task Description
2.1 Task Formulation
2.2 Data Collection
2.3 Annotation Schema
2.4 Human Annotation
3 Evaluation Results
3.1 Evaluation Metrics
3.2 Submission Results
4 Representative Systems
5 Conclusion
References
Overview of the NLPCC 2022 Shared Task: Dialogue Text Analysis (DTA)
1 Introduction
2 Task Description
2.1 Dialogue Topic Extraction (DTE)
2.2 Dialogue Summary Generation (DSG)
3 Data Construction
4 Evaluation Metrics
5 Evaluation Results
6 Conclusion
References
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Natural Language Processing and Chinese Computing

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