Prompt for Extraction? PAIE: Prompting Argument Interaction for Event Argument Extraction (2202.12109v2)

Abstract: In this paper, we propose an effective yet efficient model PAIE for both sentence-level and document-level Event Argument Extraction (EAE), which also generalizes well when there is a lack of training data. On the one hand, PAIE utilizes prompt tuning for extractive objectives to take the best advantages of Pre-trained LLMs (PLMs). It introduces two span selectors based on the prompt to select start/end tokens among input texts for each role. On the other hand, it captures argument interactions via multi-role prompts and conducts joint optimization with optimal span assignments via a bipartite matching loss. Also, with a flexible prompt design, PAIE can extract multiple arguments with the same role instead of conventional heuristic threshold tuning. We have conducted extensive experiments on three benchmarks, including both sentence- and document-level EAE. The results present promising improvements from PAIE (3.5\% and 2.3\% F1 gains in average on three benchmarks, for PAIE-base and PAIE-large respectively). Further analysis demonstrates the efficiency, generalization to few-shot settings, and effectiveness of different extractive prompt tuning strategies. Our code is available at https://github.com/mayubo2333/PAIE.

• The paper introduces PAIE, a novel prompt-based model that significantly improves Event Argument Extraction by leveraging Pre-trained Language Models and capturing interactions among arguments.
• PAIE utilizes multi-role prompts to capture argument interactions and employs a bipartite matching loss for optimal span assignment, effectively handling complex document-level extraction.
• Experiments demonstrate that PAIE achieves substantial F1 score gains on standard EAE benchmarks, showcasing its robustness for both sentence-level and document-level extractions.

Insightful Overview of "Prompt for Extraction? PAIE: Prompting Argument Interaction for Event Argument Extraction"

The paper introduces PAIE, a model designed to improve Event Argument Extraction (EAE) through a prompt-based methodology. PAIE stands out by tackling both sentence-level and document-level EAE, leveraging the capabilities of Pre-trained LLMs (PLMs) to enhance extraction efficacy, even when faced with limited training data.

Key Contributions

1. Prompt Tuning Approach: PAIE innovatively applies prompt tuning for extractive tasks, an area previously dominated by classification and generative tasks. This approach utilizes PLMs to derive role-specific queries, allowing the effective selection of argument spans within the text. This formulation capitalizes on the pretraining knowledge embedded in LLMs.
2. Argument Interaction Capturing: The model introduces a mechanism to capture interactions among arguments using multi-role prompts. This is crucial for accurately identifying and categorizing event arguments, particularly in complex document-level scenarios where arguments might span several sentences.
3. Optimization with Bipartite Matching Loss: PAIE includes a joint optimization strategy using a bipartite matching loss, which ensures optimal span assignment. This is particularly beneficial when dealing with multiple arguments bearing the same role, eliminating the need for manual threshold tuning typically required in such cases.
4. Flexible Prompt Design: The model proposes a flexible prompt design, capable of handling multi-argument extraction efficiently. This is achieved without sacrificing accuracy, as demonstrated by the improvements in F1 scores across various benchmarks.

Experimental Results

Experiments conducted on three standard benchmarks for EAE — ACE05, RAMS, and WIKIEVENTS — show significant improvements in F1 scores, illustrating the robustness and efficacy of PAIE. For instance, PAIE achieves an average F1 gain of 3.5% in its base configuration, underscoring its capability to handle both sentence and document-level extractions effectively.

Implications and Future Directions

The development of PAIE has substantial implications in the field of NLP, particularly in enhancing the extraction of structured information from unstructured text. By successfully integrating prompt-based learning into the EAE paradigm, PAIE offers a framework that could be adapted and extended to other NLP tasks that require nuanced understanding and processing of textual data.

For future developments, PAIE sets a foundation for exploring deeper integration of semantic understanding within prompt-based methods. Further refinement could involve extending the prompt design space or integrating additional contextual knowledge to handle a broader range of linguistic phenomena.

In summary, PAIE embodies a significant advancement in EAE, proposing innovative solutions to longstanding challenges in the field. Its contributions highlight the potential for prompt-based methodologies to enhance accuracy and efficiency in complex NLP tasks.