Constructing a Comprehensive Bilingual Information Extraction Corpus: IEPILE

Grunnleggende konsepter

IEPILE is a comprehensive bilingual (English and Chinese) information extraction instruction corpus containing approximately 0.32B tokens, constructed by collecting and cleaning 33 existing datasets and introducing a schema-based instruction generation strategy to address the limitations of existing IE datasets.

Sammendrag

The paper introduces IEPILE, a large-scale bilingual (English and Chinese) information extraction (IE) instruction corpus. The authors address the limitations of existing IE datasets, which are often small in scale, fragmented, and lack standardized schema.
Key highlights:

Data Collection and Cleaning: The authors collected 26 English and 7 Chinese IE datasets, covering tasks such as Named Entity Recognition (NER), Relation Extraction (RE), and Event Extraction (EE). They employed standardization procedures to maintain data quality and format uniformity.

Schema-Based Instruction Generation: The authors introduce a schema-based instruction generation strategy to address two key issues: (1) Schema Query Disparity - inconsistency in the number of schema queries between training and evaluation, and (2) Semantic Confusion - co-occurrence of semantically similar schemas within instructions. They propose a hard negative schema construction method and a batched instruction generation approach to mitigate these issues.

Experimental Results: The authors fine-tune large language models (LLMs) such as Baichuan, LLaMA, and Qwen using IEPILE and demonstrate improved zero-shot performance compared to baselines, verifying the effectiveness of the dataset.

Analysis: The authors investigate the impact of inconsistent schema queries and the importance of the hard negative schema dictionary in enhancing model performance, especially for tasks with semantically similar schemas.

The IEPILE dataset and pre-trained models are open-sourced, aiming to provide valuable support to the NLP community.

Statistikk

The IEPILE dataset contains approximately 0.32B tokens.

Sitater

"To this end, we introduce IEPILE, a comprehensive bilingual (English and Chinese) IE instruction corpus, which contains approximately 0.32B tokens."
"We introduce schema-based instruction generation to unearth a large-scale corpus."
"Experimental results on LLaMA, Baichuan and Qwen demonstrate that using IEPILE can enhance the performance of LLMs for IE, especially the zero-shot generalization."

Viktige innsikter hentet fra

IEPile

by Honghao Gui,... klokken arxiv.org 04-09-2024

https://arxiv.org/pdf/2402.14710.pdf

Dypere Spørsmål

How can the IEPILE dataset be extended to cover more languages and domains beyond the current focus on English and Chinese?

To extend the IEPILE dataset to cover more languages and domains, several steps can be taken:

Language Expansion: Collaborate with multilingual experts to source and annotate data in additional languages. This can involve leveraging existing multilingual datasets and translating them into the desired languages to ensure consistency and quality.

Domain Diversification: Engage domain experts to identify key areas for expansion. Collect domain-specific datasets and apply the schema-based instruction generation strategy to create structured data for these new domains.

Collaborative Efforts: Partner with research institutions, organizations, and linguistic experts globally to contribute data from various languages and domains. This collaborative approach can help in scaling up the dataset efficiently.

Annotation Consistency: Ensure consistency in annotation guidelines and quality across languages and domains. Implement rigorous quality control measures to maintain the integrity of the dataset.

Continuous Updates: Regularly update the dataset with new data to keep it relevant and reflective of the evolving language use and domain-specific information.

By following these strategies, the IEPILE dataset can be expanded to encompass a wider range of languages and domains, making it more comprehensive and valuable for diverse information extraction tasks.

What are the potential limitations of the schema-based instruction generation approach, and how can it be further improved to handle more complex and open-ended information extraction tasks?

Limitations of the schema-based instruction generation approach include:

Semantic Ambiguity: The approach may struggle with highly ambiguous or context-dependent schemas, leading to potential errors in instruction generation.

Scalability: Handling a large number of schemas and their variations can be challenging, especially in open-ended tasks where new schemas may emerge.

Generalization: The model's ability to generalize to unseen schemas or tasks may be limited if the training data does not adequately cover the full spectrum of possible schemas.

To improve the approach for more complex tasks:

Dynamic Schema Generation: Implement a mechanism to dynamically generate schemas based on the input data, allowing the model to adapt to new schemas and tasks.

Hierarchical Schema Representation: Introduce a hierarchical schema representation that captures relationships between schemas, enabling the model to understand complex schema structures.

Adaptive Learning: Incorporate reinforcement learning or self-supervised learning techniques to adapt the model's schema generation process based on feedback and task performance.

Transfer Learning: Utilize transfer learning to fine-tune the model on a diverse set of tasks and schemas, enhancing its ability to handle a wide range of information extraction tasks.

By addressing these limitations and incorporating these improvements, the schema-based instruction generation approach can become more robust and effective in handling complex and open-ended information extraction tasks.

Given the growing importance of large language models in information extraction, how can the IEPILE dataset be leveraged to develop more advanced and versatile IE systems that can adapt to diverse real-world scenarios?

To leverage the IEPILE dataset for developing advanced and versatile IE systems, the following strategies can be employed:

Fine-tuning Models: Use the IEPILE dataset to fine-tune large language models on a diverse set of information extraction tasks, enabling the models to adapt to various real-world scenarios.

Multi-Task Learning: Implement multi-task learning techniques using the IEPILE dataset to train models on multiple information extraction tasks simultaneously, enhancing their versatility and adaptability.

Continual Learning: Incorporate continual learning approaches to allow models to adapt to new data and tasks over time, ensuring they remain effective in evolving real-world scenarios.

Ensemble Methods: Combine multiple models trained on different subsets of the IEPILE dataset using ensemble methods to create a more robust and versatile IE system.

Interactive Learning: Introduce interactive learning paradigms where the model can receive feedback from users or domain experts to improve its performance and adaptability in real-world scenarios.

By implementing these strategies and leveraging the rich and diverse data in the IEPILE dataset, developers can create IE systems that are not only advanced and versatile but also capable of adapting effectively to the complexities of diverse real-world information extraction tasks.