Papers with ACE
Query and Extract: Refining Event Extraction as Type-oriented Binary Decoding (2022.findings-acl)
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| Challenge: | Existing approaches to event extraction are limited to a set of pre-defined types. |
| Approach: | They propose a natural language query framework that uses event types and argument roles to extract candidate triggers and arguments from input text. |
| Outcome: | The proposed framework outperforms existing methods on zero-shot event extraction. |
Action-Concentrated Embedding Framework: This Is Your Captain Sign-tokening (2024.lrec-main)
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| Challenge: | ACE is a new sign token embedding framework that tracks a signer’s actions based on human posture estimation and captures the token embeds using a short-time Fourier transform. |
| Approach: | They propose a novel sign token embedding framework that tracks a signer’s actions based on human posture estimation and a dedicated notation system tailored for sign language. |
| Outcome: | The proposed framework outperforms previous studies in translation performance against a disaster sign language dataset and improves by up to 5.79% for BLEU-4 and 5.46% for ROUGE-L metric. |
When ACE met KBP: End-to-End Evaluation of Knowledge Base Population with Component-level Annotation (L18-1)
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| Challenge: | Automating constructing a Knowledge Base from unstructured text is a goal of natural language processing. |
| Approach: | They propose a method to evaluate a Knowledge Base population from unstructured text . they propose bootstrap resampling to provide statistical significance to the results . |
| Outcome: | The proposed method uses component-level annotations to evaluate Cold Start KBP . it also uses bootstrap resampling to provide statistical significance to the results reported . |
MovieCORE: COgnitive REasoning in Movies (2025.emnlp-main)
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Gueter Josmy Faure, Min-Hung Chen, Jia-Fong Yeh, Ying Cheng, Hung-Ting Su, Yung-Hao Tang, Shang-Hong Lai, Winston H. Hsu
| Challenge: | MovieCORE is a video question answering dataset that focuses on surface-level comprehension. |
| Approach: | They propose a video question-answer dataset that uses large language models as thought agents to generate and refine high-quality question-anchor pairs. |
| Outcome: | The proposed model improves model reasoning capabilities post-training by 25% . the proposed model is based on a large language model and is scalable to a wide range of tasks . |
Document-Level Event Argument Extraction by Conditional Generation (2021.naacl-main)
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| Challenge: | Existing event extraction models have been limited to the sentence level . this formulation signifies a misalignment between the information seeking behavior and the informative seeking behavior. |
| Approach: | They propose a document-level neural event argument extraction model by formulating the task as conditional generation following event templates. |
| Outcome: | The proposed model achieves 7.6% F1 and 5.7% F1 over the best baseline on the document-level event extraction dataset WikiEvents and 9.3% F1 on the informative argument extraction task. |
Entity-Relation Extraction as Multi-Turn Question Answering (P19-1)
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| Challenge: | Identifying entities and their relations is the prerequisite of extracting structured knowledge from unstructured raw texts. |
| Approach: | They propose a new paradigm for the task of entity-relation extraction . they cast the task as a multi-turn question answering problem . |
| Outcome: | The proposed paradigm significantly outperforms previous best models on the ACE and CoNLL04 datasets. |
Evaluating Zero-Shot Event Structures: Recommendations for Automatic Content Extraction (ACE) Annotations (2023.acl-short)
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| Challenge: | Zero-shot event extraction (EE) methods infer richly structured event records from unstructured text data, based on a user-supplied natural language specification and no training examples. |
| Approach: | They propose recommendations for future evaluations so the research community can better utilize ACE as an event evaluation resource. |
| Outcome: | The proposed methods can be used to evaluate zero-shot and other low-supervision EE methods, considering up to 32% of correctly identified arguments and 25% of correctly ignored event mentions as false negatives. |
RED-ACE: Robust Error Detection for ASR using Confidence Embeddings (2022.emnlp-main)
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| Challenge: | ASR Error Detection (AED) models post-process the output of Automatic Speech Recognition systems, in order to detect transcription errors. |
| Approach: | They propose to use ASR model's word-level confidence scores to combine ASR models with transcribed text to improve AED performance. |
| Outcome: | The proposed models combine the confidence scores and transcribed text into a contextualized representation. |
Textual Entailment for Event Argument Extraction: Zero- and Few-Shot with Multi-Source Learning (2022.findings-naacl)
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| Challenge: | Recent work shows that Relation Extraction tasks can be recasted as Textual Entailment tasks using verbalizations. |
| Approach: | They propose to recasted RE tasks as Textual Entailment tasks using verbalizations . they show that entailment reduces the need for manual annotation to 50% and 20% . |
| Outcome: | The proposed method reduces the need for manual annotation to 50% and 20% in event argument extraction tasks while achieving the same performance as with full training. |
Global Constraints with Prompting for Zero-Shot Event Argument Classification (2023.findings-eacl)
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| Challenge: | Existing zero-shot trigger extraction models require annotations, which is not practical for open-domain applications. |
| Approach: | They propose to use global constraints with prompting to tackle event argument classification without annotation and task-specific training. |
| Outcome: | The proposed model outperforms the best zero-shot baselines by 12.5% and 10.9% F1 on ACE and ERE with given argument spans and by 4.3% and 3.3% F1 without given argument spas. |
GENEVA: Benchmarking Generalizability for Event Argument Extraction with Hundreds of Event Types and Argument Roles (2023.acl-long)
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| Challenge: | Existing benchmarking datasets for Event Argument Extraction (EAE) cover less than 40 event types and 25 entity-centric argument roles. |
| Approach: | They propose to use a large and diverse EAE ontology to create a semantic role labeling dataset for EAE that incorporates 115 events and 220 argument roles. |
| Outcome: | The proposed ontology concludes with 115 events and 220 argument roles, with a significant portion of roles not being entities. |
Automated Concatenation of Embeddings for Structured Prediction (2021.acl-long)
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| Challenge: | Recent work shows that better word representations can be obtained by concatenating different types of embeddings. |
| Approach: | They propose to automate the process of finding better concatenated embeddings for structured prediction tasks by concatending different types of embeddables. |
| Outcome: | The proposed approach outperforms baselines and achieves state-of-the-art with fine-tuned embeddings on 6 tasks and 21 datasets. |
Towards Few-Shot Event Mention Retrieval: An Evaluation Framework and A Siamese Network Approach (2020.lrec-1)
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| Challenge: | Existing methods for event extraction are "one size fits all" and are not adaptable to new event types or domains of interest. |
| Approach: | They propose a few-shot Event Mention Retrieval task to retrieve event mentions from text . they use existing event datasets such as ACE and a Siamese Network approach . |
| Outcome: | The proposed approach lowers the bar of specifying event-centric information needs. |
COMPACT: Building Compliance Paralegals via Clause Graph Reasoning over Contracts (2026.eacl-long)
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| Challenge: | Existing legal NLP benchmarks focus on single-clause tasks, such as ContractNLI and CUAD. |
| Approach: | They propose a framework that models cross-clause dependencies through structured clause graphs by extracting deontic-temporal entities from clauses and constructs typed relationship graphs capturing definitional dependencies, exception hierarchies, and temporal sequences. |
| Outcome: | The proposed framework extracts deontic-temporal entities from clauses and constructs typed relationship graphs capturing definitional dependencies, exception hierarchies, and temporal sequences. |
The CLARIN Knowledge Centre for Atypical Communication Expertise (2020.lrec-1)
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| Challenge: | ACE is a new knowledge center for Atypical communication experts . it is located at the Centre for Language and Speech Technology (CLST) at Radboud University . |
| Approach: | They introduce a new CLARIN Knowledge Center called the K-Centre for Atypical Communication Expertise (ACE) ACE closely collaborates with The Language Archive at the Max Planck Institute for Psycholinguistics to safeguard GDPR-compliant data storage and access. |
| Outcome: | The new CLARIN Knowledge Center is the K-Centre for Atypical Communication Expertise (ACE) ACE closely collaborates with The Language Archive (TLA) at the Max Planck Institute for Psycholinguistics in order to safeguard GDPR-compliant data storage and access. |
EDEN: A Dataset for Event Detection in Norwegian News (2024.lrec-main)
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Samia Touileb, Jeanett Murstad, Petter Mæhlum, Lubos Steskal, Lilja Charlotte Storset, Huiling You, Lilja Øvrelid
| Challenge: | EDEN is the first dataset annotated with event information at the sentence level for the Norwegian language. |
| Approach: | They propose to annotate Norwegian news text and transcribed speech using ACE event schema. |
| Outcome: | The proposed dataset is the first annotated dataset for Norwegian, with a language-specific annotation process. |
Towards Better Question Generation in QA-based Event Extraction (2024.findings-acl)
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| Challenge: | True. True. EE aims to extract event-related information from unstructured texts. |
| Approach: | They propose a reinforcement learning method that evaluates the quality of a question and provides clear guidance to QA models. |
| Outcome: | The proposed method generates generalizable, high-quality, and context-dependent questions and provides clear guidance to QA models. |
Extracting Financial Events from Raw Texts via Matrix Chunking (2024.lrec-main)
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| Challenge: | Event Extraction (EE) is widely used in the Chinese financial field to provide valuable structured information. |
| Approach: | They propose a task which extracts financial events from raw texts and an efficient method called MACK. |
| Outcome: | The proposed method is fault-tolerant and can visualize interactions among text components. |
ACE: A LLM-based Negotiation Coaching System (2024.emnlp-main)
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| Challenge: | The rapid progress of LLMs has led to the development of more sophisticated AI tutoring systems. |
| Approach: | They develop an LLM-based assistant for coaching negotiation that provides users with targeted feedback for improvement. |
| Outcome: | The proposed system improves negotiation performance significantly compared to a system that doesn’t provide feedback and one which uses an alternative method. |