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Mirror: A Universal Framework for Various Information Extraction Tasks (2311.05419v2)

Published 9 Nov 2023 in cs.CL and cs.AI

Abstract: Sharing knowledge between information extraction tasks has always been a challenge due to the diverse data formats and task variations. Meanwhile, this divergence leads to information waste and increases difficulties in building complex applications in real scenarios. Recent studies often formulate IE tasks as a triplet extraction problem. However, such a paradigm does not support multi-span and n-ary extraction, leading to weak versatility. To this end, we reorganize IE problems into unified multi-slot tuples and propose a universal framework for various IE tasks, namely Mirror. Specifically, we recast existing IE tasks as a multi-span cyclic graph extraction problem and devise a non-autoregressive graph decoding algorithm to extract all spans in a single step. It is worth noting that this graph structure is incredibly versatile, and it supports not only complex IE tasks, but also machine reading comprehension and classification tasks. We manually construct a corpus containing 57 datasets for model pretraining, and conduct experiments on 30 datasets across 8 downstream tasks. The experimental results demonstrate that our model has decent compatibility and outperforms or reaches competitive performance with SOTA systems under few-shot and zero-shot settings. The code, model weights, and pretraining corpus are available at https://github.com/Spico197/Mirror .

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Authors (10)
  1. Tong Zhu (43 papers)
  2. Junfei Ren (2 papers)
  3. Zijian Yu (5 papers)
  4. Mengsong Wu (8 papers)
  5. Guoliang Zhang (5 papers)
  6. Xiaoye Qu (62 papers)
  7. Wenliang Chen (33 papers)
  8. Zhefeng Wang (39 papers)
  9. Baoxing Huai (28 papers)
  10. Min Zhang (630 papers)
Citations (12)
View on Semantic Scholar

Summary

An Overview of Mirror: A Universal Framework for Information Extraction

The paper "Mirror: A Universal Framework for Various Information Extraction Tasks" presents an innovative approach to unifying Information Extraction (IE) tasks through a proposed framework called Mirror. The authors address the prevalent issue within the NLP domain of handling diverse data formats and task variations, which have traditionally limited efficiency and versatility in building comprehensive information extraction systems. This paper introduces a method that reformulates these tasks into a unified paradigm that employs multi-slot tuples and cyclic graphs, thereby enhancing the scope and adaptability of IE models.

Key Contributions

  1. Unified Multi-Slot Tuple Framework: The authors propose representing various IE tasks as multi-slot tuple extraction problems. This abstraction is operationalized through the transformation of these structures into multi-span cyclic graphs, which are decoded using a non-autoregressive algorithm. This approach allows the system to handle a range of tasks, from complex entity recognition to relation extraction and machine reading comprehension, all within a singular framework.
  2. Non-Autoregressive Decoding: Mirror introduces a non-autoregressive graph decoding algorithm that efficiently handles the extraction of all spans corresponding to a task in a single step. This design choice significantly improves the model's inference speed while maintaining competitive accuracy compared to state-of-the-art models, particularly in scenarios with limited training data, such as few-shot and zero-shot settings.
  3. Corpus Construction for Pretraining: A comprehensive corpus consisting of 57 datasets was manually assembled for model pretraining, spanning 8 different types of downstream tasks. This resource facilitates the model's ability to generalize across different information extraction tasks, bolstering its capacity to perform in few-shot and zero-shot contexts.
  4. Performance Evaluation: The framework was evaluated across 30 datasets covering 8 tasks. The results exhibit that Mirror not only competes closely with the state-of-the-art systems but often surpasses them in compatibility and efficacy across various scenarios. This includes advances in traditionally difficult tasks such as multi-span discontinuous Named Entity Recognition (NER) and n-ary relation extraction.

Results and Comparisons

The experiments conducted demonstrate that Mirror achieves competitive performance in traditional IE tasks. For instance, the model reported an F1 score of up to 94.25 on the NYT dataset, indicating robust relationship extraction capabilities. Notably, Mirror's non-autoregressive design achieved significant speed advantages over autoregressive models, with a reported speed-up of more than 30 times when processing the CoNLL03 dataset.

The analysis of few-shot and zero-shot efficacy further emphasizes Mirror’s strength in learning representations that translate well to new, unseen data domains, a requisite feature for adapting to real-world applications quickly.

Implications and Future Directions

By framing IE tasks in a unified and extendable manner, the Mirror framework offers practical benefits in developing NLP applications that are not only faster but also scalable across various domains and languages. This simplification potentially reduces the computational resources required for deployment and might enable broader applicability across different computational environments and platforms.

However, beyond performance metrics, the Mirror framework also opens pathways for further research into the integration of multi-modal data inputs and more nuanced schema-guided information extraction methods. It posits the potential for exploring deeper semantic understanding in natural language processing, which could catalyze advancements in AI comprehension capabilities.

In sum, "Mirror: A Universal Framework for Various Information Extraction Tasks" provides a significant contribution to the field of NLP by advancing the state of universal IE systems, harnessing flexibility, speed, and efficiency, which are pivotal for the next generation of intelligent information processing systems.

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