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Papers from top conferences and journals for event extraction in recent years

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# Papers for Event Extraction
Papers from top conferences and journals for event extraction in recent years.

In order to show more information for each paper, we take a sentence from the abstract which can express the main purpose in the paper.

# Table of Contents

<summary><b>Expand Table of Contents</b></summary><blockquote><p align="justify">

- [ACL](#ACL)
- [EMNLP](#EMNLP)
- [NAACL](#NAACL)
- [COLING](#COLING)
- [AAAI](#AAAI)
- [LREC](#LREC)
- [Other_Conferences](#Other_Conferences)
- [Journals](#Journals)
</p></blockquote>

## ACL

### 2021

1. **MLBiNet: A Cross-Sentence Collective Event Detection Network.**
  _Dongfang Lou, Zhilin Liao, Shumin Deng, Ningyu Zhang, Huajun Chen._
  ACL 2021.
  [paper](https://aclanthology.org/2021.acl-long.373.pdf)
  
    > In this paper, we reformulate it as a Seq2Seq task and propose a Multi-Layer Bidirectional Network (MLBiNet) to capture the document-level association of events and
semantic information simultaneously.
  
  
2. **Trigger is Not Sufficient: Exploiting Frame-aware Knowledge for Implicit Event Argument Extraction.**
  _Kaiwen Wei, Sun Xian, Zequn Zhang, Jingyuan Zhang, Zhi Guo, Li Jin._
  ACL 2021.
  [paper](https://aclanthology.org/2021.acl-long.360.pdf)
  
    > In this paper, we exploit frame-aware knowledge for extracting implicit event arguments. Specifically, we introduce a curriculum knowledge distillation strategy, FEAE, to train an MRC model that could focus on frame-aware information to identify implicit arguments.

3. **Unleash GPT-2 Power for Event Detection.**
  _Amir Pouran Ben Veyseh, Viet Dac Lai, Franck Dernoncourt, and Thien Huu Nguyen._
  ACL 2021.
  [paper](https://aclanthology.org/2021.acl-long.490.pdf)
  
    > We propose a novel method for augmenting training data for ED using the samples generated by the language model GPT-2. To avoid noises in the generated data, we propose a novel teacher-student architecture in a multi-task learning framework.

4. **CLEVE: Contrastive Pre-training for Event Extraction.**
  _Ziqi Wang, Xiaozhi Wang, Xu Han, Yankai Lin, Lei Hou, Zhiyuan Liu, Peng Li, Juanzi Li, Jie Zhou._
  ACL 2021.
  [paper](https://aclanthology.org/2021.acl-long.491.pdf)
  
    > In this paper, we propose CLEVE, a contrastive pre-training framework for event extraction to utilize the rich event knowledge lying in large unsupervised data.

5. **OntoED: Low-resource Event Detection with Ontology Embedding.**
  _Shumin Deng, Ningyu Zhang, Luoqiu Li, Hui Chen, Huaixiao Tou, Mosha Chen, Fei Huang, Huajun Chen._
  ACL 2021.
  [paper](https://aclanthology.org/2021.acl-long.220.pdf)
  
    > We formulate ED as a process of event ontology population: linking event instances to pre-defined event types in event ontology, and propose a novel ED framework entitled OntoED with ontology embedding. We enrich event ontology with linkages among event types, and further induce more event-event correlations.

6. **Document-level Event Extraction via Heterogeneous Graph-based Interaction Model with a Tracker.**
  _Runxin Xu, Tianyu Liu, Lei Li and Baobao Chang._
  ACL 2021.
  [paper](https://aclanthology.org/2021.acl-long.274.pdf)
  
    > We introduce Heterogeneous Graph-based Interaction Model with a Tracker (GIT). GIT uses a heterogeneous graph interaction network to model global interactions among sentences and entity mentions. GIT also uses a Tracker to track the extracted records to consider global interdependency during extraction.

7. **Document-level Event Extraction via Parallel Prediction Networks.**
  Hang Yang, Dianbo Sui, Yubo Chen, Kang Liu, Jun Zhao, Taifeng Wang._
  ACL 2021.
  [paper](https://aclanthology.org/2021.acl-long.492.pdf)
  
    > In this paper, we propose an end-to-end model, which can extract structured events from a document in a parallel manner. Specifically, we first introduce a document-level encoder to obtain the document-aware representations. Then, a multi-granularity non-autoregressive decoder is used to generate events in parallel. Finally, to train the entire model, a matching loss function is proposed, which can bootstrap a global optimization.

8. **Zero-shot Event Extraction via Transfer Learning: Challenges and Insights.**
  _Qing Lyu, Hongming Zhang, Elior Sulem, Dan Roth._
  ACL 2021.
  [paper](https://aclanthology.org/2021.acl-short.42.pdf)
  
    > In this work, we explore the possibility of zeroshot event extraction by formulating it as a set of Textual Entailment (TE) and/or Question Answering (QA) queries (e.g. “A city was attacked” entails “There is an attack”), exploiting pretrained TE/QA models for direct transfer.

### 2020

1. **Improving Event Detection via Open-domain Trigger Knowledge.**
  _Meihan Tong, Bin Xu, Shuai Wang, Yixin Cao, Lei Hou, Juanzi Li, Jun Xie._
  ACL 2020.
  [paper](https://www.aclweb.org/anthology/2020.acl-main.522.pdf)
  
    > We propose a novel Enrichment Knowledge Distillation (EKD) model to leverage external open-domain trigger knowledge to reduce the in-built biases to frequent trigger words in annotations.
  
  
2. **A Two-Step Approach for Implicit Event Argument Detection.**
  _Zhisong Zhang, Xiang Kong, Zhengzhong Liu, Xuezhe Ma, Eduard Hovy._
  ACL 2020.
  [paper](https://www.aclweb.org/anthology/2020.acl-main.667.pdf)
  
    > We adopt a two-step approach, decomposing the problem into two sub-problems: argument head-word detection and head-to-span expansion. Evaluated on the recent RAMS dataset, our model achieves overall better performance than a strong sequence labeling baseline.

3. **Extensively Matching for Few-shot Learning Event Detection.**
  _Viet Dac Lai, Franck Dernoncourt, Thien Huu Nguyen._
  ACL 2020.
  [paper](https://www.aclweb.org/anthology/2020.nuse-1.5.pdf)
  
    > We formulate event detection as a few-shot learning problem to enable to extend event detection to new event types. We propose two novel loss factors that matching examples in the support set to provide more training signals to the model. Moreover, these training signals can be applied in many metric-based few-shot learning models.

4. **Document-Level Event Role Filler Extraction using Multi-Granularity Contextualized Encoding.**
  _Xinya Du, Claire Cardie._
  ACL 2020.
  [paper](https://www.aclweb.org/anthology/2020.acl-main.714.pdf)
  
    > We first investigate how end-toend neural sequence models (with pre-trained language model representations) perform on document-level role filler extraction, as well as how the length of context captured affects the models’ performance. To dynamically aggregate information captured by neural representations learned at different levels of granularity (e.g., the sentence- and paragraph-level), we propose a novel multi-granularity reader.

5. **Global Locality in Biomedical Relation and Event Extraction.**
  _Elaheh ShafieiBavani, Antonio Jimeno Yepes, Xu Zhong, David Martinez Iraola._
  ACL 2020.
  [paper](https://www.aclweb.org/anthology/2020.bionlp-1.21.pdf)
  
    > We propose an approach to both relation and event extraction, for simultaneously predicting relationships between all mention pairs in a text. We also perform an empirical study to discuss different network setups for this purpose.

6. **Towards Open Domain Event Trigger Identification using Adversarial Domain Adaptation.**
  _Aakanksha Naik, Carolyn Rose._
  ACL 2020.
  [paper](https://www.aclweb.org/anthology/2020.acl-main.681.pdf)
  
    > We tackle the task of building supervised event trigger identification models which can generalize better across domains. Our work leverages the adversarial domain adaptation (ADA) framework to introduce domain-invariance.

7. **Cross-media Structured Common Space for Multimedia Event Extraction.**
  _Manling Li, Alireza Zareian, Qi Zeng, Spencer Whitehead, Di Lu, Heng Ji, Shih-Fu Chang._
  ACL 2020.
  [paper](https://www.aclweb.org/anthology/2020.acl-main.230.pdf)
  
    > We introduce a new task, MultiMedia Event Extraction (M2E2), which aims to extract events and their arguments from multimedia documents.

8. **Exploring Interpretability in Event Extraction: Multitask Learning of a Neural Event Classifier and an Explanation Decoder.**
  _Zheng Tang, Gustave Hahn-Powell, Mihai Surdeanu._
  ACL 2020.
  [paper](https://www.aclweb.org/anthology/2020.acl-srw.23.pdf)
  
    > We propose an interpretable approach for event extraction that mitigates the tension between generalization and interpretability by jointly training for the two goals. Our approach uses an encoder-decoder architecture, which jointly trains a classifier for event extraction, and a rule decoder that generates syntactico-semantic rules that explain the decisions of the event classifier.
   
   
### 2019

1. **Exploring Pre-trained Language Models for Event Extraction and Generation.**
  _Sen Yang, Dawei Feng, Linbo Qiao, Zhigang Kan, Dongsheng Li._
  ACL 2019.
  [paper](https://www.aclweb.org/anthology/P19-1522.pdf)
  
      > We first propose an event extraction model to overcome the roles overlap problem by separating the argument prediction in terms of roles. Moreover, to address the problem of insufficient training data, we propose a method to automatically generate labeled data by editing prototypes and screen out generated samples by ranking the quality.
  
  
2. **Distilling Discrimination and Generalization Knowledge for Event Detection via ∆-Representation Learning.**
  _Yaojie Lu, Hongyu Lin, Xianpei Han, Le Sun._
  ACL 2019.
  [paper](https://www.aclweb.org/anthology/P19-1429.pdf)
  
    > This paper proposes a ∆-learning approach to distill discrimination and generalization knowledge by effectively decoupling, incrementally learning and adaptively fusing event representation.

3. **Cost-sensitive Regularization for Label Confusion-aware Event Detection.**
  _Hongyu Lin, Yaojie Lu, Xianpei Han, Le Sun._
  ACL 2019.
  [paper](https://www.aclweb.org/anthology/P19-1521.pdf)
  
    > This paper proposes cost-sensitive regularization, which can force the training procedure to concentrate more on optimizing confusing type pairs. Specifically, we introduce a costweighted term into the training loss, which penalizes more on mislabeling between confusing label pairs. Furthermore, we also propose two estimators which can effectively measure such label confusion based on instance-level or population-level statistics.

4. **Rapid Customization for Event Extraction.**
  _Yee Seng Chan, Joshua Fasching, Haoling Qiu, and Bonan Min._
  ACL 2019.
  [paper](https://www.aclweb.org/anthology/P19-3006.pdf)
  
    > We present a system for rapidly customizing event extraction capability to find new event types (what happened) and their arguments (who, when, and where).
    
5. **Literary Event Detection.**
  _Matthew Sims, Jong Ho Park, David Bamman._
  ACL 2019.
  [paper](https://www.aclweb.org/anthology/P19-1353.pdf)
  
    > In this work we present a new dataset of literary events—events that are depicted as taking place within the imagined space of a novel.
    
6. **Open Domain Event Extraction Using Neural Latent Variable Models.**
  _Xiao Liu, Heyan Huang, Yue Zhang._
  ACL 2019.
  [paper](https://www.aclweb.org/anthology/P19-1276.pdf)
  
    > We consider open domain event extraction, the task of extracting unconstraint types of events from news clusters. A novel latent variable neural model is constructed, which is scalable to very large corpus.

### 2018

1. **Document Embedding Enhanced Event Detection with Hierarchical and Supervised Attention.**
  _Yue Zhao, Xiaolong Jin, Yuanzhuo Wang, Xueqi Cheng._
  ACL 2018.
  [paper](https://www.aclweb.org/anthology/P18-2066.pdf)
  
    > We proposed a hierarchical and supervised attention based and document embedding enhanced Bi-RNN method, called DEEB-RNN, for event detection. We explored different strategies to construct gold word and sentence-level attentions to focus on event information.

2. **Self-regulation: Employing a Generative Adversarial Network to Improve Event Detection.**
  _Yu Hong Wenxuan Zhou Jingli Zhang Qiaoming Zhu Guodong Zhou._
  ACL 2018.
  [paper](https://www.aclweb.org/anthology/P18-1048.pdf)
  
    > We propose a self-regulated learning approach by utilizing a generative adversarial network to generate spurious features. On the basis, we employ a recurrent network to eliminate the fakes.

3. **Zero-Shot Transfer Learning for Event Extraction.**
  _Lifu Huang, Heng Ji, Kyunghyun Cho, Ido Dagan, Sebastian Riedel, Clare R. Voss._
  ACL 2018.
  [paper](https://www.aclweb.org/anthology/P18-1201.pdf)
  
    > We design a transferable architecture of structural and compositional neural networks to jointly represent and map event mentions and types into a shared semantic space.
 
 
4. **Nugget Proposal Networks for Chinese Event Detection.**
  _Hongyu Lin, Yaojie Lu, Xianpei Han, Le Sun._
  ACL 2018.
  [paper](https://www.aclweb.org/anthology/P18-1145.pdf)
  
    > We propose Nugget Proposal Networks (NPNs), which can solve the word-trigger mismatch problem by directly proposing entire trigger nuggets centered at each character regardless of word boundaries.

5. **DCFEE: A Document-level Chinese Financial Event Extraction System based on Automatically Labeled Training Data.**
  _Hang Yang, Yubo Chen, Kang Liu, Yang Xiao, Jun Zhao._
  ACL 2018.
  [paper](https://www.aclweb.org/anthology/P18-4009.pdf)
  
    >We present an event extraction framework to detect event mentions and extract events from the document-level financial news.
    
    
6. **Biomedical Event Extraction Using Convolutional Neural Networks and Dependency Parsing.**
  _Jari Bjorne, Tapio Salakoski._
  ACL 2018.
  [paper](https://www.aclweb.org/anthology/W18-2311.pdf)
  
    >We develop a convolutional neural network that can be used for both event and relation extraction. We use a linear representation of the input text, where information is encoded with various vector space embeddings. Most notably, we encode the parse graph into this linear space using dependency path embeddings.  
    
    
7. **Economic Event Detection in Company-Specific News Text.**
  _Gilles Jacobs, Els Lefever, Veronique Hoste._
  ACL 2018.
  [paper](https://www.aclweb.org/anthology/W18-3101.pdf)
  
    >This paper presents a dataset and supervised classification approach for economic event detection in English news articles.

### 2017

1. **Exploiting Argument Information to Improve Event Detection via Supervised Attention Mechanisms.**
  _Shulin Liu, Yubo Chen, Kang Liu, Jun Zhao._
  ACL 2017.
  [paper](https://www.aclweb.org/anthology/P17-1164.pdf)
  
    >We propose to exploit argument information explicitly for ED via supervised attention mechanisms.   
    
    
2. **Automatically Labeled Data Generation for Large Scale Event Extraction.**
  _Yubo Chen, Shulin Liu, Xiang Zhang, Kang Liu, Jun Zhao._
  ACL 2017.
  [paper](https://www.aclweb.org/anthology/P17-1038.pdf)
  
    >We propose to automatically label training data for event extraction via world knowledge and linguistic knowledge, which can detect key arguments and trigger words for each event type and employ them to label events in texts automatically.  
    
    
3. **English Event Detection With Translated Language Features.**
  _Sam Wei, Igor Korostil, Joel Nothman, Ben Hachey._
  ACL 2017.
  [paper](https://www.aclweb.org/anthology/P17-2046.pdf)
  
    >We propose novel radical features from automatic translation for event extraction.

### 2016

1. **A Language-Independent Neural Network for Event Detection.**
  _Xiaocheng Feng, Lifu Huang, Duyu Tang, Bing Qin, Heng Ji, Ting Liu._
  ACL 2016.
  [paper](https://www.aclweb.org/anthology/P16-2011.pdf)
  
    > We develop a hybrid neural network to capture both sequence and chunk information from specific contexts, and use them to train an event detector for multiple languages without any manually encoded features.
  
  
2. **Event Nugget Detection with Forward-Backward Recurrent Neural Networks.**
  _Reza Ghaeini, Xiaoli Z. Fern, Liang Huang, Prasad Tadepalli._
  ACL 2016.
  [paper](https://www.aclweb.org/anthology/P16-2060.pdf)
  
    > We instead use forward-backward recurrent neural networks (FBRNNs) to detect events that can be either words or phrases.

3. **Leveraging FrameNet to Improve Automatic Event Detection.**
  _Shulin Liu, Yubo Chen, Shizhu He, Kang Liu, Jun Zhao._
  ACL 2016.
  [paper](https://www.aclweb.org/anthology/P16-1201.pdf)
  
    > We propose a global inference approach to detect events in FN. Further, based on the detected results, we analyze possible mappings from frames to event-types.

4. **Liberal Event Extraction and Event Schema Induction.**
  _Lifu Huang, Taylor Cassidy, Xiaocheng Feng, Heng Ji, Clare R. Voss, Jiawei Han, Avirup Sil._
  ACL 2016.
  [paper](https://www.aclweb.org/anthology/P16-1025.pdf)
  
    > We propose a brand new “Liberal” Event Extraction paradigm to extract events and discover event schemas from any input corpus simultaneously.
    
    
5. **RBPB: Regularization-Based Pattern Balancing Method for Event Extraction.**
  _Lei Sha, Jing Liu, Chin-Yew Lin, Sujian Li, Baobao Chang, Zhifang Sui._
  ACL 2016.
  [paper](https://www.aclweb.org/anthology/P16-1116.pdf)
  
    > This paper proposes a Regularization-Based Pattern Balancing Method (RBPB). Inspired by the progress in representation learning, we use trigger embedding, sentence-level embedding and pattern features together as our features for trigger classification so that the effect of patterns and other useful features can be balanced.

### 2015

1. **Event Extraction via Dynamic Multi-Pooling Convolutional Neural Networks.**
  _Yubo Chen, Liheng Xu, Kang Liu, Daojian Zeng, Jun Zhao._
  ACL 2015.
  [paper](https://www.aclweb.org/anthology/P15-1017.pdf)
  
    > This paper proposes a novel event-extraction method, which aims to automatically extract lexical-level and sentence-level features without using complicated NLP tools.

2. **Event Detection and Domain Adaptation with Convolutional Neural Networks.**
  _Thien Huu Nguyen, Ralph Grishman._
  ACL 2015.
  [paper](https://www.aclweb.org/anthology/P15-2060.pdf)
  
    > We study the event detection problem using convolutional neural networks (CNNs) that overcome the two fundamental limitations of the traditional feature-based approaches to this task: complicated feature engineering for rich feature sets and error propagation from the preceding stages which generate these features.
    
    
3. **A Domain-independent Rule-based Framework for Event Extraction.**
  _Marco A. Valenzuela-Escarcega, Gus Hahn-Powell, Thomas Hicks, Mihai Surdeanu._
  ACL 2015.
  [paper](https://www.aclweb.org/anthology/P15-4022.pdf)
  
    > We describe the design, development, and API of ODIN (Open Domain INformer), a domainindependent, rule-based event extraction (EE) framework.

4. **Disease Event Detection based on Deep Modality Analysis.**
  _Yoshiaki Kitagawa, Mamoru Komachi, Eiji Aramaki, Naoaki Okazaki, and Hiroshi Ishikawa._
  ACL 2015.
  [paper](https://www.aclweb.org/anthology/P15-3005.pdf)
  
    > This study proposes the use of modality features to improve disease event detection from Twitter messages, or “tweets”.

## EMNLP

### 2020

1. **Event Extraction by Answering (Almost) Natural Questions.**
  _Xinya Du, Claire Cardie._
  EMNLP 2020.
  [paper](https://www.aclweb.org/anthology/2020.emnlp-main.49.pdf)
  
    > We introduce a new paradigm for event extraction by formulating it as a question answering (QA) task that extracts the event arguments in an end-to-end manner.

2. **Event Extraction as Machine Reading Comprehension.**
  _Jian Liu, Yubo Chen, Kang Liu, Wei Bi, Xiaojiang Liu._
  EMNLP 2020.
  [paper](https://www.aclweb.org/anthology/2020.emnlp-main.128.pdf)
  
    > We propose a new learning paradigm of EE, by explicitly casting it as a machine reading comprehension problem (MRC). Our approach includes an unsupervised question generation process, which can transfer event schema into a set of natural questions, followed by a BERTbased question-answering process to retrieve answers as EE results.

3. **MAVEN: A Massive General Domain Event Detection Dataset.**
  _Xiaozhi Wang, Ziqi Wang, Xu Han, Wangyi Jiang, Rong Han, Zhiyuan Liu, Juanzi Li, Peng Li, Yankai Lin, Jie Zhou._
  EMNLP 2020.
  [paper](https://www.aclweb.org/anthology/2020.emnlp-main.129.pdf)
  
    > We present a MAssive eVENt detection dataset (MAVEN), which contains 4, 480 Wikipedia documents, 118, 732 event mention instances, and 168 event types. MAVEN alleviates the data scarcity problem and covers much more general event types. We reproduce the recent state-of-the-art ED models and conduct a thorough evaluation on MAVEN.

4. **Event Detection: Gate Diversity and Syntactic Importance Scores for Graph Convolution Neural Networks.**
  _Viet Dac Lai, Tuan Ngo Nguyen, Thien Huu Nguyen._
  EMNLP 2020.
  [paper](https://www.aclweb.org/anthology/2020.emnlp-main.435.pdf)
  
    > We propose a novel gating mechanism to filter noisy information in the hidden vectors of the GCN models for ED based on the information from the trigger candidate. We also introduce novel mechanisms to achieve the contextual diversity for the gates and the importance score consistency for the graphs and models in ED.

5. **Incremental Event Detection via Knowledge Consolidation Networks.**
  _Pengfei Cao, Yubo Chen, Jun Zhao, Taifeng Wang._
  EMNLP 2020.
  [paper](https://www.aclweb.org/anthology/2020.emnlp-main.52.pdf)
  
    > Existing incremental learning methods cannot handle semantic ambiguity and training data imbalance problems between old and new classes in the task of incremental event detection. In this paper, we propose a Knowledge Consolidation Network (KCN) to address the above issues.

6. **Biomedical Event Extraction as Sequence Labeling.**
  _Alan Ramponi, Rob van der Goot, Rosario Lombardo, Barbara Plank._
  EMNLP 2020.
  [paper](https://www.aclweb.org/anthology/2020.emnlp-main.431.pdf)
  
    > We introduce Biomedical Event Extraction as Sequence Labeling (BEESL), a joint endto-end neural information extraction model. BEESL recasts the task as sequence labeling, taking advantage of a multi-label aware encoding strategy and jointly modeling the intermediate tasks via multi-task learning.

7. **Introducing a New Dataset for Event Detection in Cybersecurity Texts.**
  _Hieu Man Duc Trong, Duc Trong Le, Amir Pouran Ben Veyseh, Thuat Nguyen, and Thien Huu Nguyen._
  EMNLP 2020.
  [paper](https://www.aclweb.org/anthology/2020.emnlp-main.433.pdf)
  
    > We present a new dataset CySecED for event detection in the cybersecurity domain. Our dataset is manually annotated for 30 event types and provides sufficient data to develop deep learning models for this task.
    
    
8. **Semi-supervised New Event Type Induction and Event Detection.**
  _Lifu Huang, Heng Ji._
  EMNLP 2020.
  [paper](https://www.aclweb.org/anthology/2020.emnlp-main.53.pdf)
  
    > We have designed a semi-supervised vector quantized variational autoencoder approach which automatically learns a discrete representations for each seen and unseen type and predict a type for each candidate trigger.

9. **Biomedical Event Extraction as Multi-turn Question Answering.**
  _Xing David Wang, Leon Weber, Ulf Leser._
  EMNLP 2020.
  [paper](https://www.aclweb.org/anthology/2020.louhi-1.10.pdf)
  
    > We present an alternative approach where the detection of relationships between entities is described uniformly as questions, which are iteratively answered by a question answering (QA) system based on the domain-specific language model SciBERT.

10. **Event Extraction as Multi-turn Question Answering.**
  _Fayuan Li, Weihua Peng, Yuguang Chen, Quan Wang, Lu Pan, Yajuan Lyu, Yong Zhu._
  Findings-EMNLP 2020.
  [paper](https://www.aclweb.org/anthology/2020.findings-emnlp.73.pdf)
  
    > Our approach, MQAEE, casts the extraction task into a series of reading comprehension problems, by which it extracts triggers and arguments successively from a given sentence. A history answer embedding strategy is further adopted to model question answering history in the multi-turn process.   
    
    
11. **Edge-Enhanced Graph Convolution Networks for Event Detection with Syntactic Relation.**
  _Shiyao Cui, Bowen Yu, Tingwen Liu, Zhenyu Zhang, Xuebin Wang, Jinqiao Shi._
  Findings-EMNLP 2020.
  [paper](https://www.aclweb.org/anthology/2020.findings-emnlp.211.pdf)
  
    > We propose a novel architecture named Edge-Enhanced Graph Convolution Networks (EE-GCN), which simultaneously exploits syntactic structure and typed dependency label information to perform ED.
    
    
12. **Graph Transformer Networks with Syntactic and Semantic Structures for Event Argument Extraction.**
  _Amir Pouran Ben Veyseh, Tuan Ngo Nguyen, Thien Huu Nguyen._
  Findings-EMNLP 2020.
  [paper](https://www.aclweb.org/anthology/2020.findings-emnlp.326.pdf)
  
    > We propose a novel model for EAE that exploits both syntactic and semantic structures of the sentences with the Graph Transformer Networks (GTNs) to learn more effective sentence structures for EAE. In addition, we introduce a novel inductive bias based on information bottleneck to improve generalization of the EAE models. 
    
    
13. **Biomedical Event Extraction with Hierarchical Knowledge Graphs.**
  _Kung-Hsiang Huang, Mu Yang, Nanyun Peng._
  Findings-EMNLP 2020.
  [paper](https://www.aclweb.org/anthology/2020.findings-emnlp.114.pdf)
  
    > We propose to incorporate domain knowledge from Unified Medical Language System (UMLS) to a pre-trained language model via a hierarchical graph representation encoded by a proposed Graph Edgeconditioned Attention Networks (GEANet).
    
    
14. **How Does Context Matter? On the Robustness of Event Detection with Context-Selective Mask Generalization.**
  _Jian Liu, Yubo Chen, Kang Liu, Yantao Jia, Zhicheng Sheng._
  Findings-EMNLP 2020.
  [paper](https://www.aclweb.org/anthology/2020.findings-emnlp.229.pdf)
  
    > This paper focuses on the robustness of ED. We highlight three stark cases showing the brittleness of existing ED models. Then we propose a new approach called context-selective masking generalization shedding lights on robustifying an ED model.
    
    
15. **Resource-Enhanced Neural Model for Event Argument Extraction.**
  _Jie Ma, Shuai Wang∗._
  Findings-EMNLP 2020.
  [paper](https://www.aclweb.org/anthology/2020.findings-emnlp.318.pdf)
  
    > We present a new model which provides the best results in the EAE task. The model can generate trigger-aware argument representations, incorporate syntactic information (via dependency parses), and handle the role overlapping problem with rolespecific argument decoder.

### 2019

1. **HMEAE: Hierarchical Modular Event Argument Extraction.**
  _Xiaozhi Wang, Ziqi Wang, Xu Han, Zhiyuan Liu, Juanzi Li, Peng Li, Maosong Sun, Jie Zhou, Xiang Ren._
  EMNLP 2019.
  [paper](https://www.aclweb.org/anthology/D19-1584.pdf)
  
    > We propose a Hierarchical Modular Event Argument Extraction (HMEAE) model, to provide effective inductive bias from the concept hierarchy of event argument roles.

2. **Event Detection with Multi-Order Graph Convolution and Aggregated Attention.**
  _Haoran Yan, Xiaolong Jin, Xiangbin Meng, Jiafeng Guo, Xueqi Cheng._
  EMNLP 2019.
  [paper](https://www.aclweb.org/anthology/D19-1582.pdf)
  
    > This paper proposes a new method for event detection, which uses a dependency tree based graph convolution network with aggregative attention to explicitly model and aggregate multi-order syntactic representations in sentences.

3. **Event Detection with Trigger-Aware Lattice Neural Network.**
  _Ning Ding, Ziran Li, Zhiyuan Liu, Hai-Tao Zheng, Zibo Lin._
  EMNLP 2019.
  [paper](https://www.aclweb.org/anthology/D19-1033.pdf)
  
    > We propose a novel framework TLNN for event detection, which can simultaneously address the problems of trigger-word mismatch and polysemous triggers.

4. **Doc2EDAG: An End-to-End Document-level Framework for Chinese Financial Event Extraction.**
  _Shun Zheng, Wei Cao, Wei Xu, Jiang Bian._
  EMNLP 2019.
  [paper](https://www.aclweb.org/anthology/D19-1032.pdf)
  
    >  We propose a novel end-to-end model, Doc2EDAG, which can generate an entity-based directed acyclic graph to fulfill the document-level EE(DEE) effectively.

5. **Entity, Relation, and Event Extraction with Contextualized Span Representations.**
  _David Wadden, Ulme Wennberg, Yi Luan, Hannaneh Hajishirzi._
  EMNLP 2019.
  [paper](https://www.aclweb.org/anthology/D19-1585.pdf)
  
    > Our framework (called DYGIE++) accomplishes all tasks by enumerating, refining, and scoring text spans designed to capture local (withinsentence) and global (cross-sentence) context.

6. **Neural Cross-Lingual Event Detection with Minimal Parallel Resources.**
  _Jian Liu, Yubo Chen, Kang Liu, Jun Zhao._
  EMNLP 2019.
  [paper](https://www.aclweb.org/anthology/D19-1068.pdf)
  
    >  We propose a new method for cross-lingual ED, demonstrating a minimal dependency on parallel resources.

7. **Financial Event Extraction Using Wikipedia-Based Weak Supervision.**
  _Liat Ein-Dor, Ariel Gera, Orith Toledo-Ronen, Alon Halfon, Benjamin Sznajder, Lena Dankin, Yonatan Bilu, Yoav Katz and Noam Slonim._
  EMNLP 2019.
  [paper](https://www.aclweb.org/anthology/D19-5102.pdf)
  
    >  This work is in line with this latter approach, leveraging relevant Wikipedia sections to extract weak labels for sentences describing economic events.

8. **Cross-lingual Structure Transfer for Relation and Event Extraction.**
  _Ananya Subburathinam, Di Lu1, Heng Ji, Jonathan May, Shih-Fu Chang, Avirup Sil, Clare Voss._
  EMNLP 2019.
  [paper](https://www.aclweb.org/anthology/D19-1030.pdf)
  
    >  We exploit relation- and event-relevant language-universal features, leveraging both symbolic (including part-of-speech and dependency path) and distributional (including type representation and contextualized representation) information.

9. **Open Event Extraction from Online Text using a Generative Adversarial Network.**
  _Rui Wang, Deyu Zhou, Yulan He._
  EMNLP 2019.
  [paper](https://www.aclweb.org/anthology/D19-1027.pdf)
  
    >  We propose an event extraction model based on Generative Adversarial Nets, called Adversarial-neural Event Model (AEM).

10. **Extending Event Detection to New Types with Learning from Keywords.**
  _Viet Dac Lai, Thien Huu Nguyen._
  EMNLP 2019.
  [paper](https://www.aclweb.org/anthology/D19-5532.pdf)
  
    >  We introduce a novel feature-based attention mechanism for convolutional neural networks for event detection in the new formulation.
 
 
### 2018

1. **Jointly Multiple Events Extraction via Attention-based Graph Information Aggregation.**
  _Xiao Liu, Zhunchen Luo, Heyan Huang._
  EMNLP 2018.
  [paper](https://www.aclweb.org/anthology/D18-1156.pdf)
  
    > We propose a novel Jointly Multiple Events Extraction (JMEE) framework to jointly extract multiple event triggers and arguments by introducing syntactic shortcut arcs to enhance information flow and attention-based graph convolution networks to model graph information.

2. **Collective Event Detection via a Hierarchical and Bias Tagging Networks with Gated Multi-level Attention Mechanisms.**
  _Yubo Chen, Hang Yang, Kang Liu, Jun Zhao, Yantao Jia._
  EMNLP 2018.
  [paper](https://www.aclweb.org/anthology/D18-1158.pdf)
  
    > This paper proposes a novel framework for event detection, which can automatically extract and dynamically integrate sentence-level and documentlevel information and collectively detect multiple events in one sentence.

3. **Exploiting Contextual Information via Dynamic Memory Network for Event Detection.**
  _Shaobo Liu, Rui Cheng, Xiaoming Yu, Xueqi Cheng._
  EMNLP 2018.
  [paper](https://www.aclweb.org/anthology/D18-1127.pdf)
  
    > We proposed the TD-DMN model which utilizes the multi-hop mechanism of the dynamic memory network to better capture the contextual information for the event trigger detection task.

4. **Event Detection with Neural Networks: A Rigorous Empirical Evaluation.**
  _J. Walker Orr, Prasad Tadepalli, Xiaoli Fern._
  EMNLP 2018.
  [paper](https://www.aclweb.org/anthology/D18-1122.pdf)
  
    > We present a novel GRU-based model that combines syntactic information along with temporal structure through an attention mechanism. 
    
  
5. **Similar but not the Same: Word Sense Disambiguation Improves Event Detection via Neural Representation Matching.**
  _Weiyi Lu, Thien Huu Nguyen._
  EMNLP 2018.
  [paper](https://www.aclweb.org/anthology/D18-1517.pdf)
  
      > We propose a method to transfer the knowledge learned on WSD to ED by matching the neural representations learned for the two tasks.

### 2016

1. **Modeling Skip-Grams for Event Detection with Convolutional Neural Networks.**
  _Thien Huu Nguyen, Ralph Grishman._
  EMNLP 2016.
  [paper](https://www.aclweb.org/anthology/D16-1085.pdf)
  
    > We propose to improve the current CNN models for ED by introducing the non-consecutive convolution.

2. **Event Detection and Co-reference with Minimal Supervision.**
  _Haoruo Peng, Yangqiu Song, Dan Roth._
  EMNLP 2016.
  [paper](https://www.aclweb.org/anthology/D16-1038.pdf)
  
    > This paper proposes a novel event detection and co-reference approach with minimal supervision, addressing some of the key issues slowing down progress in research on events, including the difficulty to annotate events and their relations.
    
    
### 2015

1. **Joint Event Trigger Identification and Event Coreference Resolution with Structured Perceptron.**
  _Jun Araki, Teruko Mitamura._
  EMNLP 2015.
  [paper](https://www.aclweb.org/anthology/D15-1247.pdf)
  
    > This paper proposes a document-level structured learning model that simultaneously identifies event triggers and resolves event coreference.

2. **Event Detection and Factuality Assessment with Non-Expert Supervision.**
  _Kenton Lee, Yoav Artzi, Yejin Choi, and Luke Zettlemoyer._
  EMNLP 2015.
  [paper](https://www.aclweb.org/anthology/D15-1189.pdf)
  
    > We studied event detection and scalar factuality prediction, demonstrating that non-expert annotator can, in aggregate, provide high-quality data and introducing simple models that perform well on each task.

## NAACL

### 2021

1. **Document-Level Event Argument Extraction by Conditional Generation.**
  _Sha Li, Heng Ji, Jiawei Han._
  NAACL 2021.
  [paper](https://aclanthology.org/2021.naacl-main.69.pdf)
  
    > We propose a document-level neural event argument extraction model by formulating the task as conditional generation following event templates. We also compile a new document-level event extraction benchmark dataset WIKIEVENTS which includes complete event and coreference annotation.

2. **GTN-ED: Event Detection Using Graph Transformer Networks.**
  _Sanghamitra Dutta, Liang Ma, Tanay Kumar Saha, Di Lu, Joel Tetreault, Alejandro Jaimes._
  NAACL 2021.
  [paper](https://aclanthology.org/2021.textgraphs-1.13.pdf)
  
    > In this work, we propose a novel framework for incorporating both dependencies and their labels using a recently proposed technique called Graph Transformer Networks(GTN). We integrate GTNs to leverage dependency relations on two existing homogeneousgraph-based models, and demonstrate an improvement in the F1 score on the ACE dataset.

3. **Document-level Event Extraction with Efficient End-to-end Learning of Cross-event Dependencies.**
  _Kung-Hsiang Huang, Nanyun Peng._
  NAACL 2021.
  [paper](https://aclanthology.org/2021.nuse-1.4.pdf)
  
    > In this paper, we propose an end-to-end model leveraging Deep Value Networks (DVN), a structured prediction algorithm, to efficiently capture cross-event dependencies for document-level event extraction.
    
    
### 2019

1. **Adversarial Training for Weakly Supervised Event Detection.**
  _Xiaozhi Wang, Xu Han, Zhiyuan Liu, Maosong Sun, Peng Li._
  NAACL 2019.
  [paper](https://www.aclweb.org/anthology/N19-1105.pdf)
  
    > We build a large event-related candidate set with good coverage and then apply an adversarial training mechanism to iteratively identify those informative instances from the candidate set and filter out those noisy ones.

2. **Event Detection without Triggers.**
  _Shulin Liu, Yang Li, Xinpeng Zhou, Tao Yang, Feng Zhang._
  NAACL 2019.
  [paper](https://www.aclweb.org/anthology/N19-1080.pdf)
  
    > We propose a novel framework dubbed as Type-aware Bias Neural Network with Attention Mechanisms (TBNNAM), which encodes the representation of a sentence based on target event types.

3. **Multilingual Entity, Relation, Event and Human Value Extraction.**
  _Manling Li, Ying Lin, Joseph Hoover, Spencer Whitehead, Clare R. Voss, Morteza Dehghani, Heng Ji._
  NAACL 2019.
  [paper](https://www.aclweb.org/anthology/N19-4019.pdf)
  
    > This paper demonstrates a state-of-the-art endto-end multilingual (English, Russian, and Ukrainian) knowledge extraction system that can perform entity discovery and linking, relation extraction, event extraction, and coreference.

4. **SEDTWik: Segmentation-based Event Detection from Tweets using Wikipedia.**
  _Keval M. Morabia, Neti Lalita Bhanu Murthy, Aruna Malapati, Surender S. Samant._
  NAACL 2019.
  [paper](https://www.aclweb.org/anthology/N19-3011.pdf)
  
    > This paper presents the problems associated with event detection from tweets and a tweet-segmentation based system for event detection called SEDTWik, an extension to a previous work, that is able to detect newsworthy events occurring at different locations of the world from a wide range of categories.

5. **Biomedical Event Extraction Based on Knowledge-driven Tree-LSTM.**
  _Diya Li, Lifu Huang, Heng Ji, Jiawei Han._
  NAACL 2019.
  [paper](https://www.aclweb.org/anthology/N19-1145.pdf)
  
    > We show the effectiveness of using a KB-driven tree-structured LSTM for event extraction in biomedical domain.
    
    
### 2018

1. **Semi-Supervised Event Extraction with Paraphrase Clusters.**
  _James Ferguson, Colin Lockard, Daniel S. Weld, Hannaneh Hajishirzi._
  NAACL 2018.
  [paper](https://www.aclweb.org/anthology/N18-2058.pdf)
  
    > We present a method for self-training event extraction systems by bootstrapping additional training data.

2. **Neural Events Extraction from Movie Descriptions.**
  _Alex Tozzo, Dejan Jovanovic, Mohamed R. Amer._
  NAACL 2018.
  [paper](https://www.aclweb.org/anthology/W18-1507.pdf)
  
    > We formulate our problem using a recurrent neural network, enhanced with structural features extracted from syntactic parser, and trained using curriculum learning by progressively increasing the difficulty of the sentences.
    
    
### 2016

1. **Joint Event Extraction via Recurrent Neural Networks.**
  _Thien Huu Nguyen, Kyunghyun Cho, Ralph Grishman._
  NAACL 2016.
  [paper](https://www.aclweb.org/anthology/N16-1034.pdf)
  
    > We propose to do event extraction in a joint framework with bidirectional recurrent neural networks, thereby benefiting from the advantages of the two models as well as addressing issues inherent in the existing approaches.

2. **Joint Extraction of Events and Entities within a Document Context.**
  _Bishan Yang, Tom Mitchell._
  NAACL 2016.
  [paper](https://www.aclweb.org/anthology/N16-1033.pdf)
  
    > We propose a novel approach that models the dependencies among variables of events, entities, and their relations, and performs joint inference of these variables across a document.

3. **Bidirectional RNN for Medical Event Detection in Electronic Health Records.**
  _Abhyuday N Jagannatha, Hong Yu._
  NAACL 2016.
  [paper](https://www.aclweb.org/anthology/N16-1056.pdf)
  
    > We have shown that RNNs models like LSTM and GRU are valuable tools for extracting medical events and attributes from noisy natural language text of EHR notes.

### 2015

1. **Diamonds in the Rough: Event Extraction from Imperfect Microblog Data.**
  _Ander Intxaurrondo, Eneko Agirre, Oier Lopez de Lacalle, Mihai Surdeanu._
  NAACL 2015.
  [paper](https://www.aclweb.org/anthology/N15-1066.pdf)
  
    > We introduce a distantly supervised event extraction approach that extracts complex event templates from microblogs.

## COLING

### 2018

1. **Open-Domain Event Detection using Distant Supervision.**
  _Jun Araki, Teruko Mitamura._
  COLING 2018.
  [paper](https://www.aclweb.org/anthology/C18-1075.pdf)
  
    > This paper introduces open-domain event detection, a new event detection paradigm to address issues of prior work on restricted domains and event annotation.

### 2016

1. **Leveraging Multilingual Training for Limited Resource Event Extraction.**
  _Andrew Hsi, Yiming Yang, Jaime Carbonell, Ruochen Xu._
  COLING 2016.
  [paper](https://www.aclweb.org/anthology/C16-1114.pdf)
  
    > We propose a new event extraction approach that trains on multiple languages using a combination of both language-dependent and language-independent features, with particular focus on the case where target domain training data is of very limited size.

2. **Incremental Global Event Extraction.**
  _Alex Judea, Michael Strube._
  COLING 2016.
  [paper](https://www.aclweb.org/anthology/C16-1215.pdf)
  
    > We present an incremental approach to make the global context of the entire document available to the intra-sentential, state-of-the-art event extractor.

## AAAI

### 2020

1. **Image Enhanced Event Detection in News Articles.**
  _Meihan Tong, Shuai Wang, Yixin Cao, Bin Xu, Juaizi Li, Lei Hou, Tat-Seng Chua._
  AAAI 2020.
  [paper](https://www.aaai.org/Papers/AAAI/2020GB/AAAI-TongM.4888.pdf)
  
    > In this paper, we first contribute an image dataset supplement to ED benchmarks (i.e., ACE2005) for training and evaluation. We then propose a novel Dual Recurrent Multimodal Model, DRMM, to conduct deep interactions between images and sentences for modality features aggregation.

2. **A Human-AI Loop Approach for Joint Keyword Discovery and Expectation Estimation in Micropost Event Detection.**
  _Akansha Bhardwaj, Jie Yang, Philippe Cudre-Mauroux._
  AAAI 2020.
  [paper](https://arxiv.org/pdf/1912.00667.pdf)
  
    > This paper introduces a Human-AI loop approach to jointly discover informative keywords for model training while estimating their expectation.
    
    
### 2019    
    
1. **Exploiting the Ground-Truth: An Adversarial Imitation Based Knowledge Distillation Approach for Event Detection.**
  _Jian Liu, Yubo Chen, Kang Liu._
  AAAI 2019.
  [paper](https://wvvw.aaai.org/ojs/index.php/AAAI/article/view/4649)
  
    > We propose an adversarial imitation based knowledge distillation approach, for the first time, to tackle the challenge of acquiring knowledge from rawsentences for event detection.

2. **One for All: Neural Joint Modeling of Entities and Events.**
  _Trung Minh Nguyen, Thien Huu Nguyen._
  AAAI 2019.
  [paper](https://www.aaai.org/ojs/index.php/AAAI/article/view/4661)
  
    > We propose a novel model to jointly perform predictions for entity mentions, event triggers and arguments based on the shared hidden representations from deep learning.

### 2018   
    
1. **Graph Convolutional Networks with Argument-Aware Pooling for Event Detection.**
  _Thien Huu Nguyen, Ralph Grishman._
  AAAI 2018.
  [paper](https://ix.cs.uoregon.edu/~thien/pubs/graphConv.pdf)
  
    > We investigate a convolutional neural network based on dependency trees to perform event detection.

2. **Event Detection via Gated Multilingual Attention Mechanism.**
  _Jian Liu, Yubo Chen, Kang Liu, Jun Zhao._
  AAAI 2018.
  [paper](https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16371/16017)
  
    > We propose a novel multilingual approach — dubbed as Gated MultiLingual Attention (GMLATT) framework — to address the two issues simultaneously. 
    
    
3. **Jointly Extracting Event Triggers and Arguments by Dependency-Bridge RNN and Tensor-Based Argument Interaction.**
  _Lei Sha, Feng Qian, Baobao Chang, Zhifang Sui._
  AAAI 2018.
  [paper](https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/viewFile/16222/16157)
  
    > We propose a novel dependency bridge recurrent neural network (dbRNN) for event extraction.

4. **Scale Up Event Extraction Learning via Automatic Training Data Generation.**
  _Ying Zeng, Yansong Feng, Rong Ma, Zheng Wang, Rui Yan, Chongde Shi, Dongyan Zhao._
  AAAI 2018.
  [paper](https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/viewFile/16119/16173)
  
    > This paper has presented a novel, fast approach to automatically construct training data for event extraction with little human involvement, which in turn allows effective event extraction modeling.

### 2016   
    
1. **From Tweets to Wellness: Wellness Event Detection from Twitter Streams.**
  _Mohammad Akbari, Xia Huc, Nie Liqiang, Tat-Seng Chua._
  AAAI 2016.
  [paper](https://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/view/11931/11568)
  
    > We proposed a learning framework that utilizes content information of microblogging texts as well as the relation between event categories to extract PWE from users social posts.

## LREC

### 2020

1. **A French Corpus for Event Detection on Twitter.**
  _Beatrice Mazoyer, Julia Cage, Nicolas Herve, Celine Hudelot._
  LREC 2020.
  [paper](https://www.aclweb.org/anthology/2020.lrec-1.763.pdf)
  
    > We present Event2018, a corpus annotated for event detection tasks, consisting of 38 million tweets in French (retweets excluded) including more than 130,000 tweets manually annotated by three annotators as related or unrelated to a given event.

2. **A Dataset for Multilingual Epidemiological Event Extraction.**
  _Stephen Mutuvi, Antoine Doucet, Gael Lejeune, Moses Odeo._
  LREC 2020.
  [paper](https://www.aclweb.org/anthology/2020.lrec-1.509.pdf)
  
    > This paper proposes a corpus for the development and evaluation of tools and techniques for identifying emerging infectious disease threats in online news text. The corpus can not only be used for information extraction, but also for other natural language processing (NLP) tasks such as text classification.

3. **Event Extraction from Unstructured Amharic Text.**
  _Ephrem Tadesse, Rosa Tsegaye Aga, Kuulaa Qaqqabaa._
  LREC 2020.
  [paper](https://www.aclweb.org/anthology/2020.lrec-1.258.pdf)
  
    > In this study, we present a system that extracts an event from unstructured Amharic text. The system has designed by the integration of supervised machine learning and rule-based approaches.

4. **A Platform for Event Extraction in Hindi.**
  _Sovan Kumar Sahoo, Saumajit Saha, Asif Ekbal, Pushpak Bhattacharyya._
  LREC 2020.
  [paper](https://www.aclweb.org/anthology/2020.lrec-1.273.pdf)
  
    > In this paper, we present an Event Extraction framework for Hindi language by creating an annotated resource for benchmarking, and then developing deep learning based models to set as the baselines.

5. **Cross-Domain Evaluation of Edge Detection for Biomedical Event Extraction.**
  _Alan Ramponi, Barbara Plank, Rosario Lombardo._
  LREC 2020.
  [paper](https://www.aclweb.org/anthology/2020.lrec-1.244.pdf)
  
    > We present the first cross-domain study of edge detection for biomedical event extraction. We analyze differences between five existing gold standard corpora, create a standardized benchmark corpus, and provide a strong baseline model for edge detection.

6. **Cross-lingual Structure Transfer for Zero-resource Event Extraction.**
  _Di Lu1, Ananya Subburathinam, Heng Ji, Jonathan May, Shih-Fu Chang, Avirup Sil, Clare Voss._
  LREC 2020.
  [paper](https://www.aclweb.org/anthology/2020.lrec-1.243.pdf)
  
    > In this paper, we propose a novel cross-lingual structure transfer framework for zero-resource event extraction. Experiments on three languages show promising results without using any annotation.
    
   
   
## Other_Conferences

### IJCAI 2019

1. **Extracting Entities and Events as a Single Task Using a Transition-Based Neural Model.**
  _Junchi Zhang, Yanxia Qin, Yue Zhang, Mengchi Liu, Donghong Ji._
  IJCAI 2019.
  [paper](https://www.ijcai.org/Proceedings/2019/0753.pdf)
  
    > We build a first model to this end using a neural transition-based framework, incrementally predicting complex joint structures in a state-transition process.
    
    
### CoNLL 2019

1. **Exploiting the Entity Type Sequence to Benefit Event Detection.**
  _Yuze Ji, Youfang Lin, Jianwei Gao, Huaiyu Wan._
  CoNLL 2019.
  [paper](https://www.aclweb.org/anthology/K19-1057.pdf)
  
    > We propose a novel ED approach which learns sequential features from word sequences and entity type sequences separately, and combines these two types of sequential features with the help of a trigger-entity interaction learning module.

2. **Contextualized Cross-Lingual Event Trigger Extraction with Minimal Resources.**
  _Meryem M’hamdi , Marjorie Freedman, Jonathan May._
  CoNLL 2019.
  [paper](https://www.aclweb.org/anthology/K19-1061.pdf)
  
    > We treat event trigger extraction as a sequence tagging problem and propose a cross-lingual framework for training it without any hand-crafted features.
    
    
    
## Journals

## Neuro Computing

### 2020

1. **A neural model for joint event detection and prediction.**
  _Linmei Hu, Shuqi Yu, Bin Wu, Chao Shao, Xiaoli Li._
  Neuro Computing 2020.
  [paper](https://sci-hub.tw/https://doi.org/10.1016/j.neucom.2020.05.023)
  
    > We propose a novel neural model for joint event detection and prediction, which classifies the events to predefined types as well as predicts the next probable event by generating a sequence of words describing it. In addition, we propose a hierarchical attention mechanism to enable the model to capture important information at both word level and event level for next event prediction.

2. **A Hybrid Fine-grained Neural Network for Biomedical Event Trigger Identification.**
  _Yufeng Diao, Hongfei Lin, Liang Yang, Xiaochao Fan, Di Wu, Zhihao Yang, Jian Wang, Kan Xu._
  Neuro Computing 2020.
  [paper](https://sci-hub.tw/https://doi.org/10.1016/j.neucom.2019.09.042)
  
    > We propose a hybrid structure FBSN which consists of Fine-grained Bidirectional Long Short Term Memory (FBi-LSTM) and Support Vector Machine (SVM) to deal with the event trigger identification.

3. **Rumor events detection enhanced by encoding sentimental information into time series division and word representations.**
  _Zhihong Wang, Yi Guo._
  Neuro Computing 2020.
  [paper](https://sci-hub.tw/https://doi.org/10.1016/j.neucom.2020.01.095)
  
    > We construct a Sentiment Dictionary (SD) including a Sentiment Word Dictionary (SWD) and a Sentiment Emoticon Dictionary (SED) to capture the fine-grained human emotional reactions to different events.

## arxiv

### 2020

1. **Joint Event Extraction along Shortest Dependency Paths using Graph Convolutional Networks.**
  _Ali Balali, Masoud Asadpour, Ricardo Campos, Adam Jatowt._
  arxiv 2020 [cs.LG].
  [paper](https://arxiv.org/ftp/arxiv/papers/2003/2003.08615.pdf)
  
    >  We propose a novel joint event extraction framework that aims to extract multiple event triggers and arguments simultaneously by introducing shortest dependency path (SDP) in the dependency graph.

2. **Improving Event Detection using Contextual Word and Sentence Embeddings.**
  _Mariano Maisonnave, Fernando Delbianco, Fernando Tohm´e, Ana Maguitman, Evangelos Milios._
  arxiv 2020 [cs.CL].
  [paper](https://arxiv.org/pdf/2007.01379.pdf)
  
    > The main contribution of this paper is the design, implementation and evaluation of a recurrent neural network model for ED that combines several features.

3. **Extracting COVID-19 Events from Twitter.**
  _Shi Zong, Ashutosh Baheti, Wei Xu, Alan Ritter._
  arxiv 2020 [cs.CL].
  [paper](https://arxiv.org/pdf/2006.02567.pdf)
  
    > We present an annotated corpus of 7,500 tweets for COVID-19 events, including positive/negative tests and denied access to testing.

4. **Event Extraction Based on Deep Learning in Food Hazard Arabic Texts.**
  _Fouzi Harrag, Selmene Gueliani._
  arxiv 2020 [cs.CL].
  [paper](https://arxiv.org/pdf/2006.02567.pdf)
  
    > We proposed here a model based on deep recurrent networks to extract the events from social media feeds.
    
    
5. **Document-level Event-based Extraction Using Generative Template-filling Transformers.**
  _Xinya Du, Alexander Rush, Claire Cardie._
  arxiv 2020 [cs.CL].
  [paper](https://arxiv.org/pdf/2006.02567.pdf)
  
    > We argue that sentence-level approaches are ill-suited to the task and introduce a generative transformer-based encoder-decoder framework that is designed to model context at the document level.    
    
    
### 2019

1. **Meta-Learning with Dynamic-Memory-Based Prototypical Network for Few-Shot Event Detection.**
  _Shumin Deng, Ningyu Zhang, Jiaojian Kang, Yichi Zhang, Wei Zhang, Huajun Chen._
  arxiv 2019 [cs.CL].
  [paper](https://arxiv.org/pdf/1910.11621.pdf)
  
    >  We propose a Dynamic-Memory-Based Prototypical Network (DMB-PN), which exploits Dynamic Memory Network (DMN) to not only learn better prototypes for event types, but also produce more robust sentence encodings for event mentions.
    
    
2. **Extending Event Detection to New Types with Learning from Keywords.**
  _Viet Dac Lai, Thien Huu Nguyen._
  arxiv 2019 [cs.LG].
  [paper](https://arxiv.org/pdf/1910.11368.pdf)
  
    >  We study a novel formulation of event detection that describes types via several keywords to match the contexts in documents. This facilitates the operation of the models to new types. We introduce a novel feature-based attention mechanism for convolutional neural networks for event detection in the new formulation.

3. **Event Detection in Twitter: A Keyword Volume Approach.**
  _Ahmad Hany Hossny, Lewis Mitchell._
  arxiv 2019 [cs.SI].
  [paper](https://arxiv.org/pdf/1901.00570.pdf)
  
    >  We propose an efficient method to select the keywords frequently used in Twitter that are mostly associated with events of interest such as protests. 
    
    
4. **CONTEXT AWARENESS AND EMBEDDING FOR BIOMEDICAL EVENT EXTRACTION.**
  _Shankai Yan, Ka-Chun Wong._
  arxiv 2019 [cs.CL].
  [paper](https://arxiv.org/pdf/1905.00982.pdf)
  
    >   We proposed a bottom-up event detection framework using deep learning techniques. We built an LSTM-based model VecEntNet to construct argument embeddings for each recognized entity.

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