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. |
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Contextualized Soft Prompts for Extraction of Event Arguments (2023.findings-acl)
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| Challenge: | Existing prompt-based methods for event argument extraction rely on discrete and manually-designed prompts that cannot exploit specific context for each example. |
| Approach: | They propose a prompt-based method that introduces soft prompts to facilitate encoding of individual example context and multiple relevant documents to boost EAE. |
| Outcome: | The proposed method extensively evaluates on benchmark datasets to demonstrate its benefits with state-of-the-art performance. |
Graph Transformer Networks with Syntactic and Semantic Structures for Event Argument Extraction (2020.findings-emnlp)
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| Challenge: | Existing models for Event Argument Extraction fail to exploit semantic structures of sentences to induce effective representations for EAE. |
| Approach: | They propose a novel model that exploits syntactic and semantic structures of sentences to learn more effective sentence structures for EAE. |
| Outcome: | The proposed model improves the performance of the existing models on standard datasets. |
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. |
Neural Gibbs Sampling for Joint Event Argument Extraction (2020.aacl-main)
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| Challenge: | Existing methods for event argument extraction cannot adequately model the correlation between event arguments and their roles. |
| Approach: | They propose a Bayesian model to jointly extract event arguments using Gibbs sampling . they train two neural networks to model prior distribution and conditional distribution over event arguments . |
| Outcome: | The proposed model can achieve comparable results to existing methods on two widely-used datasets. |
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. |
Demonstration Retrieval-Augmented Generative Event Argument Extraction (2024.lrec-main)
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| Challenge: | Experimental results show that our method outperforms all strong baselines and can be generalized to various datasets. |
| Approach: | They propose a generative EAE that uses event knowledge-injected generator and demonstration retriever to generate event arguments from training data. |
| Outcome: | The proposed method outperforms baselines and can be generalized to various datasets. |
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. |
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. |
Machine Reading Comprehension as Data Augmentation: A Case Study on Implicit Event Argument Extraction (2021.emnlp-main)
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| Challenge: | Existing datasets are too small to train a model for capturing regularities underlying how event arguments are extracted. |
| Approach: | They propose to bridge implicit EAE with machine reading comprehension (MRC) by building a unified training framework and explicit data augmentation regimes via MRC. |
| Outcome: | The proposed method obtains state-of-the-art performance on two benchmarks and demonstrates superior results in a data-low scenario. |
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. |