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Few-shot Information Extraction is Here: Pre-train, Prompt and Entail

Published: 07 July 2022 Publication History

Abstract

Deep Learning has made tremendous progress in Natural Language Processing (NLP), where large pre-trained language models (PLM) fine-tuned on the target task have become the predominant tool. More recently, in a process called prompting, NLP tasks are rephrased as natural language text, allowing us to better exploit linguistic knowledge learned by PLMs and resulting in significant improvements. Still, PLMs have limited inference ability. In the Textual Entailment task, systems need to output whether the truth of a certain textual hypothesis follows from the given premise text. Manually annotated entailment datasets covering multiple inference phenomena have been used to infuse inference capabilities to PLMs.
This talk will review these recent developments, and will present an approach that combines prompts and PLMs fine-tuned for textual entailment that yields state-of-the-art results on Information Extraction (IE) using only a small fraction of the annotations. The approach has additional benefits, like the ability to learn from different schemas and inference datasets. These developments enable a new paradigm for IE where the expert can define the domain-specific schema using natural language and directly run those specifications, annotating a handful of examples in the process. A user interface based on this new paradigm will also be presented. Beyond IE, inference capabilities could be extended, acquired and applied from other tasks, opening a new research avenue where entailment and downstream task performance improve in tandem.

References

[1]
Sainz, Oscar, et al. "Label Verbalization and Entailment for Effective Zero and Few-Shot Relation Extraction." Proc. of EMNLP. 2021.
[2]
Sainz, Oscar, et al. "Textual Entailment for Event Argument Extraction: Zero- and Few-Shot with Multi-Source Learning." Proc. of NAACL findings. 2022
[3]
Sainz, Oscar, et al. "ZS4IE: A toolkit for Zero-Shot Information Extraction with simple Verbalizations." Proc. of NAACL Demo. 2022.

Cited By

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  • (2024)Syntax-Augmented Hierarchical Interactive Encoder for Zero-Shot Cross-Lingual Information ExtractionIEEE/ACM Transactions on Audio, Speech and Language Processing10.1109/TASLP.2024.348554732(4795-4809)Online publication date: 1-Jan-2024

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  1. Few-shot Information Extraction is Here: Pre-train, Prompt and Entail

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    cover image ACM Conferences
    SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval
    July 2022
    3569 pages
    ISBN:9781450387323
    DOI:10.1145/3477495
    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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    New York, NY, United States

    Publication History

    Published: 07 July 2022

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    Author Tags

    1. deep learning
    2. foundation models
    3. information extraction
    4. natural language inference
    5. natural language processing
    6. pre-trained language models
    7. textual entailment

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    • Keynote

    Funding Sources

    • IARPA
    • Basque Gobernment

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    SIGIR '22
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    • (2024)Syntax-Augmented Hierarchical Interactive Encoder for Zero-Shot Cross-Lingual Information ExtractionIEEE/ACM Transactions on Audio, Speech and Language Processing10.1109/TASLP.2024.348554732(4795-4809)Online publication date: 1-Jan-2024

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