Generating Contextualized Event Temporal Graphs from Text using a Set-Aligning Framework

Centrala begrepp

The core message of this paper is to propose a Set-Aligning Framework (SAF) that enables efficient employment of Large Language Models (LLMs) for auto-regressive event temporal graph generation. The framework incorporates novel Set Property Regularisations (SPR) and data augmentation techniques to address the challenges associated with using conventional text generation loss for this task.

Sammanfattning

The paper introduces a framework for generating event temporal graphs directly from raw text using large language models. The key insights are:

Event temporal graphs can be represented as sets of edges, where each edge describes a temporal relation between two events. However, conventional text generation models treat these sets as ordered sequences, leading to suboptimal performance.

To address this, the authors propose a Set-Aligning Framework (SAF) that incorporates:

Data augmentation by randomly shuffling the order of edges in the target sequence.
Set Property Regularisations (SPR) to encourage the model to generate the correct number of edges and avoid duplicates, as well as to align the semantics of the generated edges with the target.

The authors build a large-scale dataset for document-level event temporal graph generation using the New York Times corpus, which is more challenging than previous datasets. They also annotate a human-evaluated test set for this task.

Experiments show that the proposed SAF framework significantly improves the performance of Flan-T5-base on both the NYT dataset and existing sentence-level event temporal relation extraction datasets under zero-shot settings. The framework encourages the model to generate at least 24% more edges compared to the vanilla text generation approach.

The authors also analyze the limitations of the framework, noting that it struggles to capture temporal relations that require commonsense reasoning, as the training data is noisy and relies on rule-based event and relation extraction.

Statistik

The average number of nodes (events) in the NYT dataset is 46, and the average number of edges (temporal relations) is 58.
The average number of relations per event is 2.52 in the NYT dataset.

Citat

"Event temporal graphs have been shown as convenient and effective representations of complex temporal relations between events in text."
"Recent studies, which employ pre-trained language models to auto-regressively generate linearised graphs for constructing event temporal graphs, have shown promising results. However, these methods have often led to suboptimal graph generation as the linearised graphs exhibit set characteristics which are instead treated sequentially by language models."

Viktiga insikter från

Set-Aligning Framework for Auto-Regressive Event Temporal Graph Generation

by Xingwei Tan,... på arxiv.org 04-03-2024

https://arxiv.org/pdf/2404.01532.pdf

Djupare frågor

How can the proposed framework be extended to better capture temporal relations that require commonsense reasoning beyond the information provided in the text?

The proposed framework can be extended by incorporating external knowledge bases or ontologies that contain commonsense information. By integrating these additional sources of information, the model can access a broader range of knowledge to infer temporal relations that may not be explicitly stated in the text. For example, by leveraging a commonsense knowledge graph like ATOMIC, the model can infer implicit temporal relationships based on general world knowledge. This extension would enable the model to make more accurate predictions for events that require commonsense reasoning beyond the textual information provided.

How can the quality of the training data for event temporal graph generation be improved to further boost the performance of the proposed framework?

To enhance the quality of the training data for event temporal graph generation, several strategies can be implemented:

Human Annotation: Increase the use of human annotators to create high-quality labeled datasets. Human annotators can provide more accurate and nuanced annotations compared to automated methods like CAEVO.
Adversarial Training: Introduce adversarial examples during training to challenge the model and improve its robustness. Adversarial training can help the model learn to handle edge cases and outliers more effectively.
Active Learning: Implement an active learning strategy to iteratively select the most informative samples for annotation. This approach can help prioritize data points that are most beneficial for improving model performance.
Data Augmentation: Expand the data augmentation techniques to include a wider variety of transformations and perturbations. By exposing the model to diverse data variations, it can learn to generalize better and handle different scenarios more effectively.
Curated Datasets: Curate datasets specifically tailored to the nuances of event temporal graph generation, ensuring that the data reflects real-world complexities and challenges. This curated approach can provide more relevant and useful training examples for the model.

What other types of structured data, beyond event temporal graphs, could benefit from the Set-Aligning Framework approach?

The Set-Aligning Framework approach can be applied to various types of structured data beyond event temporal graphs, including:

Knowledge Graphs: Set-Aligning Framework can enhance the generation and alignment of edges in knowledge graphs, ensuring accurate representation of relationships between entities.
Biological Pathways: The framework can be utilized to generate and align pathways in biological networks, aiding in the understanding of complex biological processes.
Financial Networks: Set-Aligning Framework can improve the generation of financial networks, capturing intricate connections between financial entities and transactions.
Social Networks: The approach can be beneficial for modeling social networks, facilitating the generation of relationships and interactions between individuals or groups.
Supply Chain Networks: Set-Aligning Framework can optimize the representation of supply chain networks, helping to visualize and analyze the flow of goods and services across the network.

By applying the Set-Aligning Framework to these diverse types of structured data, it can enhance the accuracy, coherence, and completeness of the generated graphs or networks, leading to more effective data analysis and decision-making processes.