REAR: Reinforced Reasoning Optimization for Event Argument Extraction with Relation-Aware Support (2025.findings-emnlp)
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| Challenge: | Existing methods for EAE restrict integration of relation-level semantics, thereby overlooking the complementary cues from RE. |
| Approach: | They propose a Relation-aware EAE Reinforced optimization framework that integrates relation-level cues from RE into the Large Language Model (LLM) |
| Outcome: | The proposed framework surpasses existing decoder-only methods on the ACE-E, ACE+ and ERE benchmarks. |
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Document-Level Event Argument Extraction via Optimal Transport (2022.findings-acl)
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| Challenge: | Prior work on event-level EAE models ignore syntactic structures for documents . prior work on EE is restricted to sentence-level setting where event triggers and arguments are assumed to appear in the same sentences. |
| Approach: | They propose to employ Optimal Transport to induce structures of documents based on sentence-level syntactic structures and tailored to EAE task. |
| Outcome: | The proposed model is effective in document-level EAE, with a new constraint on unrelated context words. |
Resource-Enhanced Neural Model for Event Argument Extraction (2020.findings-emnlp)
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| Challenge: | Existing work on event argument extraction (EE) is limited due to data scarcity and lack of a model encoder. |
| Approach: | They propose to capture the long-range dependency between an event trigger and a distant event argument using unlabeled data. |
| Outcome: | Experiments on the English ACE 2005 benchmark show that the proposed method achieves a new state-of-the-art. |
Trigger is Not Sufficient: Exploiting Frame-aware Knowledge for Implicit Event Argument Extraction (2021.acl-long)
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| Challenge: | Existing methods to extract event arguments focus on learning pair-wise information between arguments and the given trigger. |
| Approach: | They propose a framework to extract event-related arguments from a given event frame-level scope. |
| Outcome: | The proposed method achieves state-of-the-art on the RAMS dataset. |
EA2E: Improving Consistency with Event Awareness for Document-Level Argument Extraction (2022.findings-naacl)
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| Challenge: | Recent work on document-level event argument extraction models each individual event in isolation and therefore causes inconsistency among extracted arguments across events. |
| Approach: | They propose an event-aware argument extraction model with augmented context to improve consistency . they hypothesize that participants tend to play consistent roles across multiple events in a document . |
| Outcome: | The proposed model improves consistency and accuracy of arguments extracted from documents. |
AMPERE: AMR-Aware Prefix for Generation-Based Event Argument Extraction Model (2023.acl-long)
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| Challenge: | Existing generation-based EAE models focus on problem re-formulation and prompt design without incorporating additional information that has been shown to be effective for classification-based models. |
| Approach: | They propose to incorporate AMR into generation-based EAE models by generating AMR-aware prefixes for every layer of the generation model. |
| Outcome: | The proposed model generates AMR-aware prefixes for every layer of the generation model and improves the generation. |
Capturing Event Argument Interaction via A Bi-Directional Entity-Level Recurrent Decoder (2021.acl-long)
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| Challenge: | Existing efforts to capture event argument interactions are limited by the argument role type information of contextual entities. |
| Approach: | They propose to capture event argument interactions as a Seq2Seq-like learning problem where a sentence with a specific event trigger is mapped to a sequence of event argument roles. |
| Outcome: | The proposed neural architecture generates argument roles by incorporating contextual entities’ argument role predictions, like a word-by-word text generation process, thereby distinguishing implicit argument distribution patterns within an event more accurately. |
Revisiting Event Argument Extraction: Can EAE Models Learn Better When Being Aware of Event Co-occurrences? (2023.acl-long)
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| Challenge: | Recent studies on event argument extraction (EAE) have not taken event co-occurrences into account. |
| Approach: | They propose to reformulate event co-occurrences as a problem of table generation and extend a SOTA prompt-based EAE model into a non-autoregressive generation framework that extracts the arguments of multiple events in parallel. |
| Outcome: | The proposed framework can extract arguments of multiple events in parallel. |
Multilingual SubEvent Relation Extraction: A Novel Dataset and Structure Induction Method (2022.findings-emnlp)
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| Challenge: | Existing methods for subevent relation extraction (SRE) focus on sequential order of words in texts to enhance representation learning. |
| Approach: | They propose a method that learns to induce effective graph structures for input texts . they use word alignment frameworks with dependency paths and optimal transport . |
| Outcome: | The proposed method is able to induce effective graph structures for input texts to boost representation learning. |
HTMR: Hybrid Token Masking Reinforcement Learning with Verifiable Rewards for Event Argument Extraction with Multi-Perspective Reasoning (2026.acl-long)
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| Challenge: | Recent work formulates EAE with large language models as a structured conditional generation task and applies Reinforcement Learning with Verifiable Rewards (RLVR) to optimize sequence-level event structures. |
| Approach: | They propose a method that selectively updates policy gradients on high-entropy forking tokens and event-critical tokens that define event structure. |
| Outcome: | The proposed method outperforms full-token and high-entropy only methods and transfers effectively as a plug-and-play approach to other tasks such as named entity recognition and relation classification. |
ULTRA: Unleash LLMs’ Potential for Event Argument Extraction through Hierarchical Modeling and Pair-wise Self-Refinement (2024.findings-acl)
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| Challenge: | Structural extraction of events within discourse is critical for event-centric understanding . document-level EAE focuses on arguments that are scattered across an entire document . ULTRA is a hierarchical framework that extracts event arguments more cost-effectively . |
| Approach: | They propose a hierarchical framework that extracts event arguments more cost-effectively . ULTRA sequentially reads text chunks of a document to generate a candidate argument set . they propose to use a supervised model to find the exact boundary of an argument . |
| Outcome: | The proposed framework outperforms strong models and ChatGPT by 9.8% when evaluated by Exact Match (EM). |