ED

Word-Label Alignment for Event Detection: A New Perspective via Optimal Transport (2022.starsem-1)

Challenge:	Traditionally, CXR report generation relies on data from a patient’s exam, overlooking valuable information from patient electronic health records.
Approach:	They propose to integrate patient data from ED records into multimodal language models that embed patient data into a language model.
Outcome:	The proposed model incorporates patient data from the MIMIC-CXR and MIMICIV-ED datasets to improve diagnostic accuracy and improves radiologist effectiveness.

Copied to clipboard

Amir Pouran Ben Veyseh, Thien Nguyen

Learning with Partial Annotations for Event Detection (2023.acl-long)

Challenge:	Event Detection (ED) is a critical task in Information Extraction.
Approach:	They propose a word-label alignment task for event detecting . they propose to incorporate word-labeled alignment biases into the equation .
Outcome:	The proposed model facilitates incorporation of word-label alignment biases on a benchmark dataset to demonstrate its effectiveness.

Copied to clipboard

Jian Liu, Dianbo Sui, Kang Liu, Haoyan Liu, Zhe Zhao

Event Detection with Trigger-Aware Lattice Neural Network (D19-1)

Challenge:	Event detection (ED) requires fully labeled and high-quality training data.
Approach:	They propose a new trigger localization formulation using contrastive learning to distinguish ground-truth triggers from contexts and show a decent robustness for addressing partial annotation noise.
Outcome:	The proposed approach achieves an F1 score of over 60% in an extreme scenario where 90% of events are unlabeled.

Copied to clipboard

Ning Ding, Ziran Li, Zhiyuan Liu, Haitao Zheng, Zibo Lin

EDM3: Event Detection as Multi-task Text Generation (2024.starsem-1)

Challenge:	Event detection is a key part of event extraction, but there are two issues with word-based models in languages without natural delimiters, such as Chinese.
Approach:	They propose a framework that can solve the problem of word- trigger mismatch . they also use an external knowledge base to model polysemous characters and words .
Outcome:	The proposed model outperforms state-of-the-art methods on two benchmark datasets and outperformed previous state- of-the art methods significantly.

Copied to clipboard

Ujjwala Anantheswaran, Himanshu Gupta, Mihir Parmar, Kuntal Pal, Chitta Baral

Improving Neural Entity Disambiguation with Graph Embeddings (P19-2)

Challenge:	Existing methods for Event Detection (ED) cannot easily leverage pre-trained semantic knowledge.
Approach:	They propose to decompose and reformulate ED and fine-tune over its atomic subtasks to enhance knowledge transfer while mitigating prediction error propagation inherent in pipelined approaches.
Outcome:	The proposed method achieves state-of-the-art performance on RAMS, MAVEN, and MLEE, while achieving 90% accuracy over rare event types.

Copied to clipboard

Özge Sevgili, Alexander Panchenko, Chris Biemann

Contextualized End-to-End Neural Entity Linking (2020.aacl-main)

Challenge:	Entity Disambiguation (ED) is the task of linking an ambiguous entity mention to a corresponding entry in a knowledge base.
Approach:	They propose a method that integrates structured information from the knowledge base with unstructured information from text-based representations.
Outcome:	The proposed method improves on a graph of hyperlinks between Wikipedia articles and a state-of-the-art neural ED model.

Copied to clipboard

Haotian Chen, Xi Li, Andrej Zukov Gregoric, Sahil Wadhwa

Neural Cross-Lingual Event Detection with Minimal Parallel Resources (D19-1)

Challenge:	a proposed entity linking model that disjointly applies MD and ED from the same contextualized BERT embeddings is able to generalize better.
Approach:	They propose an entity linking (EL) model that jointly learns mention detection (MD) and entity disambiguation (ED) they propose to use task-specific heads on top of shared BERT contextualized embeddings to learn MD and ED.
Outcome:	The proposed model achieves state-of-the-art results across a standard EL dataset and under a setting where hand-crafted candidate sets are not available.

Copied to clipboard

Jian Liu, Yubo Chen, Kang Liu, Jun Zhao

Event Detection without Triggers (N19-1)

Challenge:	Existing methods for event detection (ED) rely on high-performance machine translation systems or manually aligned documents to achieve a decent performance.
Approach:	They propose a method that uses context-dependent translation to construct a lexical mapping between different languages and a shared syntactic order event detector for multilingual co-training.
Outcome:	The proposed method performs cross-lingual transfer and tackles the extremely annotation-poor scenario.

Copied to clipboard

Shulin Liu, Yang Li, Feng Zhang, Tao Yang, Xinpeng Zhou

Entity Disambiguation with Entity Definitions (2023.eacl-main)

Challenge:	Existing approaches to event detection require annotated triggers and event types in training data.
Approach:	They propose a framework that encodes the representation of a sentence based on target event types.
Outcome:	The proposed framework achieves competitive performances compared with state-of-the-art methods.

Copied to clipboard

Luigi Procopio, Simone Conia, Edoardo Barba, Roberto Navigli

Challenge:	Entity Disambiguation (ED) is a crucial problem in Natural Language Processing (NLP).
Approach:	They propose to use Wikipedia titles as the textual representation of each candidate to improve the generalization capability over unseen patterns.
Outcome:	The proposed model improves on 2 out of 6 benchmarks and is generalized over unseen patterns.

Adversarial Training for Weakly Supervised Event Detection (N19-1)

Copied to clipboard

Xiaozhi Wang, Xu Han, Zhiyuan Liu, Maosong Sun, Peng Li

MAVEN: A Massive General Domain Event Detection Dataset (2020.emnlp-main)

Challenge:	Detecting and identifying events is an important subtask of event extraction.
Approach:	They build a large event-related candidate set with good coverage and apply an adversarial training mechanism to iteratively identify informative instances from the candidate set and filter out those noisy ones.
Outcome:	The proposed method significantly outperforms the state-of-the-art methods on two real-world datasets.

Copied to clipboard

Xiaozhi Wang, Ziqi Wang, Xu Han, Wangyi Jiang, Rong Han, Zhiyuan Liu, Juanzi Li, Peng Li, Yankai Lin, Jie Zhou

Retrieving Relevant Context to Align Representations for Cross-lingual Event Detection (2023.findings-acl)

Challenge:	Existing datasets exhibit data scarcity and limited coverage of general-domain events.
Approach:	They present a MAssive eVENt detection dataset which contains 4,480 Wikipedia documents and 168 event types.
Outcome:	The proposed dataset shows that existing methods cannot achieve promising results on the small datasets.

Copied to clipboard

Chien Nguyen, Linh Ngo, Thien Nguyen

Information Retrieval

Challenge:	Existing approaches to cross-lingual transfer learning for event detection are mixed with event-discriminative context.
Approach:	They propose a method where representations are augmented with additional context to bridge the gap between languages while enriching contextual information to facilitate ED.
Outcome:	The proposed model performs well on three languages.

Event Detection for Suicide Understanding (2022.findings-naacl)

Copied to clipboard

Luis Guzman-Nateras, Viet Lai, Amir Pouran Ben Veyseh, Franck Dernoncourt, Thien Nguyen

ZELDA: A Comprehensive Benchmark for Supervised Entity Disambiguation (2023.eacl-main)

Challenge:	Existing methods for detecting suicide-related events are limited . recognizing suicide- related events is critical to understanding the condition, authors argue .
Approach:	They propose a dataset to detect event trigger words of suicide-related events in forums . they propose 'suicideED' dataset to capture suicidal actions and ideation .
Outcome:	The proposed dataset captures suicide actions and ideation, and general risk and protective factors.

Copied to clipboard

Marcel Milich, Alan Akbik

Challenge:	Entity disambiguation (ED) is the task of disambiguating named entity mentions in text to unique entries in a knowledge base.
Approach:	They propose a benchmark for entity disambiguation that includes a unified training data set, entity vocabulary, candidate lists and challenging evaluation splits covering 8 different domains.
Outcome:	The proposed benchmark is based on a unified training data set, entity vocabulary, candidate lists and evaluation splits covering 8 different domains.

Inflecting When There’s No Majority: Limitations of Encoder-Decoder Neural Networks as Cognitive Models for German Plurals (2020.acl-main)

Copied to clipboard

Kate McCurdy, Sharon Goldwater, Adam Lopez

MINION: a Large-Scale and Diverse Dataset for Multilingual Event Detection (2022.naacl-main)

Challenge:	Encoder-decoder models can be used to generalize to inflectional morphology and generalize new words, but they fail on tasks like German number inflection, where infrequent suffixes like /-s/ can still be productively generalized.
Approach:	They propose to use a dataset to collect data from German speakers to examine whether ED models can generalize the most frequently produced plural class.
Outcome:	The proposed model does not show human-like variability or ‘regular’ extension of other plural markers.

Copied to clipboard

Amir Pouran Ben Veyseh, Minh Van Nguyen, Franck Dernoncourt, Thien Nguyen

ExtEnD: Extractive Entity Disambiguation (2022.acl-long)

Challenge:	Existing methods for ED in IE and NLP focus on feature-based models to feature-driven models.
Approach:	They propose to use a multilingual dataset to annotate events for 8 different languages . they demonstrate the challenges and transferability of ED across languages in MINION .
Outcome:	a new dataset that consistently annotates events for 8 different languages is released . the new dataset will promote future research on multilingual ED .

Copied to clipboard

Edoardo Barba, Luigi Procopio, Roberto Navigli

MUSIED: A Benchmark for Event Detection from Multi-Source Heterogeneous Informal Texts (2022.emnlp-main)

Challenge:	Entity disambiguation (ED) is a task in natural language processing that requires a large pre-trained language model to perform.
Approach:	They propose a local formulation for Entity Disambiguation (ED) that frames this task as a text extraction problem and propose two Transformer-based architectures that implement it.
Outcome:	The proposed model outperforms all its competitors in terms of data efficiency and raw performance on 4 out of 4 benchmarks.

Copied to clipboard

Xiangyu Xi, Jianwei Lv, Shuaipeng Liu, Wei Ye, Fan Yang, Guanglu Wan

Treasures Outside Contexts: Improving Event Detection via Global Statistics (2021.emnlp-main)

Challenge:	Recent efforts to classify unstructured texts into specific types have been limited in practical scenarios.
Approach:	They propose to use Chinese text conversations and phone conversations to expand event detection to the scenarios involving informal and heterogeneous texts.
Outcome:	The proposed dataset is based on user reviews, text conversations, and phone conversations in a leading e-commerce platform for food service.

Copied to clipboard

Rui Li, Wenlin Zhao, Cheng Yang, Sen Su

Improving Entity Disambiguation by Reasoning over a Knowledge Base (2022.naacl-main)

Challenge:	Existing neural-based ED models are confused by changeable contexts during testing . we propose a system that extracts statistical event features from word-event cooccurrence frequencies .
Approach:	They propose to integrate a set of statistical event features from word-event co-occurrence frequencies into the training set to cooperate with contextual features.
Outcome:	The proposed model outperforms ten strong baselines on ACE2005 and KBP2015 datasets.

Copied to clipboard

Tom Ayoola, Joseph Fisher, Andrea Pierleoni

Knowledge Representation

Challenge:	Recent work in entity disambiguation relies on a limited subset of KB facts to link entities . less common entities are prone to missing or inconsistent KB information, which is problematic for models which rely on 'one source'
Approach:	They propose an ED model which links entities by reasoning over a symbolic knowledge base in a fully differentiable fashion.
Outcome:	The proposed model outperforms state-of-the-art models on six well-established datasets by 1.3 F1 on average.

Edge-Enhanced Graph Convolution Networks for Event Detection with Syntactic Relation (2020.findings-emnlp)

Copied to clipboard

Shiyao Cui, Bowen Yu, Tingwen Liu, Zhenyu Zhang, Xuebin Wang, Jinqiao Shi

Adaptive Knowledge-Enhanced Bayesian Meta-Learning for Few-shot Event Detection (2021.findings-acl)

Challenge:	Event detection (ED) is a key subtask of information extraction.
Approach:	They propose an architecture that exploits syntactic structure and typed dependency label information to perform event detection.
Outcome:	The proposed architecture exploits syntactic structure and typed dependency label information to perform ED.

Copied to clipboard

Shirong Shen, Tongtong Wu, Guilin Qi, Yuan-Fang Li, Gholamreza Haffari, Sheng Bi

OntoED: Low-resource Event Detection with Ontology Embedding (2021.acl-long)

Challenge:	Event detection typically does not have sufficient labelled data, thus can be formulated as a few-shot learning problem.
Approach:	They propose a knowledge-based fewshot event detection method which introduces external event knowledge as the knowledge prior of event types.
Outcome:	Experiments show that the proposed method outperforms baselines by 15 F 1 points . event detection is an important task in information extraction .

Copied to clipboard

Shumin Deng, Ningyu Zhang, Luoqiu Li, Chen Hui, Tou Huaixiao, Mosha Chen, Fei Huang, Huajun Chen

How Does Context Matter? On the Robustness of Event Detection with Context-Selective Mask Generalization (2020.findings-emnlp)

Challenge:	Existing methods to ED rely on training instances and ignore correlation of event types.
Approach:	They propose a process of event ontology population linking event instances to pre-defined event types in event ontoology and ontological embedding to address these problems.
Outcome:	The proposed framework can be applied to new unseen event types by establishing linkages to existing ones.

Copied to clipboard

Jian Liu, Yubo Chen, Kang Liu, Yantao Jia, Zhicheng Sheng

Robustness in NLP

Challenge:	Existing studies focus on improving the overall performance of an ED model, but few consider the robustness of an existing model.
Approach:	They propose a new training mechanism that can effectively mine context-specific patterns for learning and robustify an ED model.
Outcome:	The proposed model can learn a complementary predictive bias with most ED models that use full context for feature learning.

Unregulated Chinese-to-English Data Expansion Does NOT Work for Neural Event Detection (2022.coling-1)

Copied to clipboard

Zhongqiu Li, Yu Hong, Jie Wang, Shiming He, Jianmin Yao, Guodong Zhou

Fine-Grained Event Trigger Detection (2021.eacl-main)

Challenge:	Experimental results show that cross-language data expansion results in performance degradation.
Approach:	They leverage cross-language data expansion and retraining to enhance neural Event Detection on English ACE corpus.
Outcome:	The proposed method improves ED performance by 1.6% over the straight data combination.

Copied to clipboard

Duong Le, Thien Huu Nguyen

Challenge:	Existing methods for Event Detection (ED) focus on a limited set of event types . existing datasets for ED focus on only 33 event types while the number of events in the TAC KBP dataset is 38.
Approach:	They propose to transform Word Sense Disambiguation's Semcor dataset into a large and high-quality dataset for ED.
Outcome:	The proposed method transforms the Semcor dataset for Word Sense Disambiguation into a large and high-quality dataset for FED.

Global Entity Disambiguation with BERT (2022.naacl-main)

Copied to clipboard

Ikuya Yamada, Koki Washio, Hiroyuki Shindo, Yuji Matsumoto

Trigger-Argument based Explanation for Event Detection (2023.findings-acl)

Challenge:	Entity disambiguation (ED) is a task of assigning mentions to referent entities in a knowledge base.
Approach:	They propose a global entity disambiguation (ED) model based on BERT . they train the model using a large entity-annotated corpus obtained from Wikipedia .
Outcome:	The proposed model can disambiguate masked entities based on words and non-masked ones at the inference time.

Copied to clipboard

Yong Guan, Jiaoyan Chen, Freddy Lecue, Jeff Pan, Juanzi Li, Ru Li

Saliency as Evidence: Event Detection with Trigger Saliency Attribution (2022.acl-long)

Challenge:	Existing works on ED use words or phrases to explain models’ inner mechanisms, but for ED, the event structure is more enlightening clues to explain model behaviors.
Approach:	They propose a Trigger-Argument based Explanation method which can utilize event structure knowledge to uncover a faithful interpretation for existing ED models at neuron level.
Outcome:	The proposed method can reveal the process by which the model predicts on the large-scale MAVEN and the widely-used ACE 2005 datasets.

Copied to clipboard

Jian Liu, Yufeng Chen, Jinan Xu

Event Detection from Social Media for Epidemic Prediction (2024.naacl-long)

Challenge:	Existing methods to ED see no differences between event types and use a single model to address them all.
Approach:	They propose a new concept termed trigger salience attribution which can explicitly quantify the underlying patterns of events.
Outcome:	The proposed model can distinguish between trigger-dependent and context-dependent types and achieve promising performance on two benchmarks.

Copied to clipboard

Tanmay Parekh, Anh Mac, Jiarui Yu, Yuxuan Dong, Syed Shahriar, Bonnie Liu, Eric Yang, Kuan-Hao Huang, Wei Wang, Nanyun Peng, Kai-Wei Chang

Entity Disambiguation via Fusion Entity Decoding (2024.naacl-long)

Challenge:	Social media is an easy-to-access platform providing timely updates about societal trends and events.
Approach:	They propose a framework to extract epidemic-related events from social media posts to provide early warnings.
Outcome:	The proposed framework can detect epidemic events for three unseen epidemics of Monkeypox, Zika, and Dengue while existing models fail miserably.

Copied to clipboard

Junxiong Wang, Ali Mousavi, Omar Attia, Ronak Pradeep, Saloni Potdar, Alexander Rush, Umar Farooq Minhas, Yunyao Li

Are we there yet? Encoder-decoder neural networks as cognitive models of English past tense inflection (P19-1)

Challenge:	Existing generative approaches demonstrate improved accuracy compared to classification approaches under the standardized ZELDA benchmark.
Approach:	They propose an encoder-decoder model to disambiguate entities with more detailed entity descriptions.
Outcome:	The proposed model outperforms existing classification models on the ZELDA benchmark and on retrieval/reader frameworks.

Copied to clipboard

Maria Corkery, Yevgen Matusevych, Sharon Goldwater

Introducing a New Dataset for Event Detection in Cybersecurity Texts (2020.emnlp-main)

Challenge:	linguistics and cognitive science have long debated the cognitive mechanisms needed to account for the English past tense.
Approach:	They propose to use an encoder-decoder model to account for the english past tense . they also show that ED models demonstrate humanlike performance in a nonce-word task .
Outcome:	The proposed model is unstable across simulations and does not fit to human data . other neural models might do better, but there is insufficient evidence to claim them .

Copied to clipboard

Hieu Man Duc Trong, Duc Trong Le, Amir Pouran Ben Veyseh, Thuat Nguyen, Thien Huu Nguyen

Parallel Iterative Edit Models for Local Sequence Transduction (D19-1)

Challenge:	a large amount of text data is produced to report and discuss cyber vulnerabilities . detecting cybersecurity events is necessary to keep us informed about the fast growing number of such events reported in text.
Approach:	They propose a dataset characterizing the manual annotation for 30 important cybersecurity event types and a large dataset to develop deep learning models.
Outcome:	The proposed dataset characterizes the manual annotation for 30 important event types and supports the modeling of document-level information to improve the performance.

Copied to clipboard

Abhijeet Awasthi, Sunita Sarawagi, Rasna Goyal, Sabyasachi Ghosh, Vihari Piratla

Text Error Correction

Challenge:	Recent approaches to local sequence transduction are based on the popular encoder-decoder model for sequence to sequence learning.
Approach:	They propose a parallel iterative edit model for the problem of local sequence transduction arising in tasks like Grammatical error correction (GEC).
Outcome:	The proposed model is faster and more accurate than the current encoder-decoder model for local sequence transduction tasks like translation and paraphrasing.

Event Detection: Gate Diversity and Syntactic Importance Scores for Graph Convolution Neural Networks (2020.emnlp-main)

Copied to clipboard

Viet Dac Lai, Tuan Ngo Nguyen, Thien Huu Nguyen

Structured Data in NLP

Challenge:	Recent studies on event detection (ED) have shown that the syntactic dependency graph can be employed in graph convolutional neural networks (GCNs) but the computation of the hidden vectors in such graph-based models is agnostic to the trigger candidate words, leaving irrelevant information for the trigger candidates.
Approach:	They propose a mechanism to filter noisy information in the hidden vectors of graph-based models based on the information from the trigger candidate.
Outcome:	The proposed model achieves state-of-the-art on two ED datasets.

Modeling Document-Level Context for Event Detection via Important Context Selection (2021.emnlp-main)

Copied to clipboard

Amir Pouran Ben Veyseh, Minh Van Nguyen, Nghia Ngo Trung, Bonan Min, Thien Huu Nguyen

Entity Embedding Completion for Wide-Coverage Entity Disambiguation (2022.findings-emnlp)

Challenge:	Existing methods for Event Detection (ED) do not encode long-range document-level context . e.g., BERT cannot encode long text-level contextual information .
Approach:	They propose a method to model document-level context for Event Detection using transformer-based language models.
Outcome:	The proposed model can predict event prediction of target sentence in document-level context . the proposed model is effective on multiple benchmark datasets .

Copied to clipboard

Daisuke Oba, Ikuya Yamada, Naoki Yoshinaga, Masashi Toyoda

Representation Learning

Challenge:	Existing state-of-the-art ED models do not address out-of vocabulary entities that are absent from training data.
Approach:	They propose to extend a state-of-the-art ED model by dynamically computing embeddings of out-ofvocabulary entities by using entity descriptions and mention contexts.
Outcome:	The proposed model performs comparable to existing models whose embeddings are trained for all candidate entities as well as embedd-free models.

Unleash GPT-2 Power for Event Detection (2021.acl-long)

Copied to clipboard

Amir Pouran Ben Veyseh, Viet Lai, Franck Dernoncourt, Thien Huu Nguyen

Coherent Entity Disambiguation via Modeling Topic and Categorical Dependency (2023.findings-emnlp)

Challenge:	Event Detection (ED) aims to recognize mentions of events and their types in text.
Approach:	They propose to exploit a pre-trained language model to generate training samples for ED.
Outcome:	The proposed model improves on multiple ED benchmark datasets and establishes state-of-the-art results.

Copied to clipboard

Zilin Xiao, Linjun Shou, Xingyao Zhang, Jie Wu, Ming Gong, Daxin Jiang

Weiyi Lu, Thien Huu Nguyen

Challenge:	Existing entity disambiguation methods struggle to capture explicit discourse-level dependencies, resulting in incoherent predictions at the abstract level.
Approach:	They propose an unsupervised variational autoencoder to extract latent topic vectors of context sentences to enhance coherence of entity predictions.
Outcome:	The proposed system achieves state-of-the-art on popular ED benchmarks with an average improvement of 1.3 F1 points.

Copied to clipboard

Improving Event Detection via Open-domain Trigger Knowledge (2020.acl-main)

Challenge:	Event detection (ED) and word sense disambiguation (WSD) are similar tasks, but they require different neural representations.
Approach:	They propose a method to transfer the knowledge learned on WSD to ED by matching neural representations learned for the two tasks.
Outcome:	The proposed method can be applied to event detection and word sense disambiguation datasets.

Copied to clipboard

Meihan Tong, Bin Xu, Shuai Wang, Yixin Cao, Lei Hou, Juanzi Li, Jun Xie

The Devil is in the Details: On the Pitfalls of Event Extraction Evaluation (2023.findings-acl)

Challenge:	Existing methods for event detecting are prone to overfitting densely labeled trigger words due to the small scale of training data.
Approach:	They propose a novel Enrichment Knowledge Distillation model to leverage external open-domain trigger knowledge to reduce in-built biases to frequent trigger words in annotations.
Outcome:	The proposed model outperforms nine strong baselines and is especially effective for unseen/sparsely labeled trigger words.

Copied to clipboard

Hao Peng, Xiaozhi Wang, Feng Yao, Kaisheng Zeng, Lei Hou, Juanzi Li, Zhiyuan Liu, Weixing Shen

Few-shot Event Detection: An Empirical Study and a Unified View (2023.acl-long)

Challenge:	Event extraction (EE) is a fundamental information extraction task aimed at extracting events from plain texts.
Approach:	They propose to specify data preprocessing, standardize outputs, and provide pipeline evaluation results to avoid these pitfalls.
Outcome:	The results show that the evaluations are reliable and lack pipeline evaluations.

Copied to clipboard

Yubo Ma, Zehao Wang, Yixin Cao, Aixin Sun

LC4EE: LLMs as Good Corrector for Event Extraction (2024.findings-acl)

Challenge:	Extensive studies have been carried out on fewshot event detection (ED) however, there are noticeable discrepancies among existing methods from three aspects.
Approach:	They propose a unified view of ED models and a better unified baseline for fair evaluation.
Outcome:	The proposed framework outperforms existing methods by a large margin on three datasets.

Copied to clipboard

Mengna Zhu, Kaisheng Zeng, JibingWu JibingWu, Lihua Liu, Hongbin Huang, Lei Hou, Juanzi Li

Evaluating Design Decisions for Dual Encoder-based Entity Disambiguation (2025.acl-long)

Challenge:	Event extraction (EE) is a critical task in natural language processing, yet deploying a practical EE system remains challenging.
Approach:	They propose to leverage the superior extraction capability of LLMs and instruction-following ability of LRMs to construct a robust and highly available EE system.
Outcome:	The proposed method can identify and correct errors in SLMs predictions based on automatically generated feedback information and improve performance.

Copied to clipboard

Susanna Rücker, Alan Akbik

Robustness Evaluation of Entity Disambiguation Using Prior Probes: the Case of Entity Overshadowing (2021.emnlp-main)

Challenge:	Entity disambiguation (ED) is the task of linking mentions in text to corresponding entries in a knowledge base.
Approach:	They propose a document-level Dual Encoder model that embeds mentions and label candidates in a shared embedding space and applies a similarity metric to predict the correct label.
Outcome:	The proposed model improves the disambiguation of ambiguous mentions of entities in text to their respective KB entries.

Copied to clipboard

Vera Provatorova, Samarth Bhargav, Svitlana Vakulenko, Evangelos Kanoulas

Robustness in NLP

Challenge:	Entity disambiguation (ED) is the last step of entity linking when candidate entities are reranked according to the context they appear in.
Approach:	They propose a dataset that includes 16K short text snippets annotated with entity mentions to evaluate EL models.
Outcome:	The proposed dataset shows that the performance of EL systems is overestimated . the results show that the EL system performance is significantly better on the ShadowLink benchmark .

Efficient Overshadowed Entity Disambiguation by Mitigating Shortcut Learning (2024.emnlp-main)

Copied to clipboard

Panuthep Tasawong, Peerat Limkonchotiwat, Potsawee Manakul, Can Udomcharoenchaikit, Ekapol Chuangsuwanich, Sarana Nutanong

Transitioning Representations between Languages for Cross-lingual Event Detection via Langevin Dynamics (2023.findings-emnlp)

Challenge:	Entity disambiguation (ED) is crucial in natural language processing tasks such as question-answering and information extraction.
Approach:	They propose a method to reduce computational overhead on overshadowed entities by addressing shortcut learning.
Outcome:	The proposed method achieves state-of-the-art performance without compromising inference speed.

Copied to clipboard

Chien Nguyen, Huy Nguyen, Franck Dernoncourt, Thien Nguyen

DiCoRe: Enhancing Zero-shot Event Detection via Divergent-Convergent LLM Reasoning (2025.emnlp-main)

Challenge:	Existing datasets for event detection (ED) are limited to a small set of popular languages due to the high cost of data annotation.
Approach:	They propose a method to develop cross-lingual transfer learning models in high-resource source languages . they aim to transition the representations for target-language examples into the source-language space .
Outcome:	Extensive experiments over three languages demonstrate the state-of-the-art in CLTL.

Copied to clipboard

Tanmay Parekh, Kartik Mehta, Ninareh Mehrabi, Kai-Wei Chang, Nanyun Peng

Low-Resource NLP

Challenge:	Understanding the complex event ontology, extracting domain-specific triggers from the passage, and structuring them appropriately overloads and limits the utility of Large Language Models (LLMs).
Approach:	They propose a divergent-convergent reasoning framework that decouples the task of ED using Dreamer and Grounder.
Outcome:	The proposed framework outperforms baselines on six datasets across five domains and nine LLMs, achieving 4–7% average gains over the best baseline.

SNaRe: Domain-aware Data Generation for Low-Resource Event Detection (2025.emnlp-main)

Copied to clipboard

Tanmay Parekh, Yuxuan Dong, Lucas Bandarkar, Artin Kim, I-Hung Hsu, Kai-Wei Chang, Nanyun Peng