EMNLP2020中事件抽取/关系抽取/NER/少样本/MRC/摘要 论文分类整理

**EMNLP2020放榜啦！**小编根据个人研究方向相关将主会和Findings论文进行了一些分类整理，(●’◡’●)整理中如有错误与疏漏，还请大家多多交流指正嗷！

主会论文榜单传送门: https://2020.emnlp.org/papers/main

Findings论文榜单传送门: https://2020.emnlp.org/papers/findings

ps.此文也在知乎发布啦！欢迎在知乎关注我: Carrie

事件抽取

主会

• Affective Event Classification with Discourse-enhanced Self-training

• Biomedical Event Extraction as Sequence Labeling

• Connecting the Dots: Event Graph Schema Induction with Path Language Modeling

• Event Extraction as Machine Reading Comprehension

• Event Extraction by Answering (Almost) Natural Questions

• Incremental Event Detection via Knowledge Consolidation Networks

• MAVEN: A Massive General Domain Event Detection Dataset

• Weakly Supervised Subevent Knowledge Acquisition

• Event Detection: Gate Diversity and Syntactic Importance Scores for Graph Convolution Neural Networks

• Introducing a New Dataset for Event Detection in Cybersecurity Texts

• Semi-supervised New Event Type Induction and Event Detection

Findings

• Edge-Enhanced Graph Convolution Networks for Event Detection with Syntactic Relation

• Event Extraction as Multi-turn Question Answering

• How Does Context Matter? On the Robustness of Event Detection with Context-Selective Mask Generalization

• Graph Transformer Networks with Syntactic and Semantic Structures for Event Argument Extraction

• Biomedical Event Extraction on Graph Edge-conditioned Attention Networks with Hierarchical Knowledge Graphs

• Resource-Enhanced Neural Model for Event Argument Extraction

• Learning to Classify Events from Human Needs Category Descriptions

关系抽取

主会

• Double Graph Based Reasoning for Document-level Relation Extraction

• FedED: Federated Learning via Ensemble Distillation for Medical Relation Extraction

• Global-to-Local Neural Networks for Document-Level Relation Extraction

• Learning from Context or Names? An Empirical Study on Neural Relation Extraction

• Let’s Stop Error Propagation in the End-to-End Relation Extraction Literature!

• Pre-training Entity Relation Encoder with Intra-span and Inter-span Information

• Recurrent Interaction Network for Jointly Extracting Entities and Classifying Relations

• Relation-aware Graph Attention Networks with Relational Position Encodings for Emotion Recognition in Conversations

• SelfORE: Self-supervised Relational Feature Learning for Open Relation Extraction

• Two are Better Than One: Joint Entity and Relation Extraction with Table-Sequence Encoders

• Denoising Relation Extraction from Document-level Distant Supervision

• Distilling Structured Knowledge for Text-Based Relational Reasoning

• Exposing Shallow Heuristics of Relation Extraction Models with Challenge D