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