mlsanityData to 14 Oct 2026

Asking the Right Questions in Low Resource Template Extraction

arXiv 2205.12643 ↗First posted 25 May 2022cs.CLcited by 4

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Abstract (arXiv)

Information Extraction (IE) researchers are mapping tasks to Question Answering (QA) in order to leverage existing large QA resources, and thereby improve data efficiency. Especially in template extraction (TE), mapping an ontology to a set of questions can be more time-efficient than collecting labeled examples. We ask whether end users of TE systems can design these questions, and whether it is beneficial to involve an NLP practitioner in the process. We compare questions to other ways of phrasing natural language prompts for TE. We propose a novel model to perform TE with prompts, and find it benefits from questions over other styles of prompts, and that they do not require an NLP background to author.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 27 of its 33 references that are in the map. First posted on arXiv 25 May 2022.

Builds on

27 of 33 references.

References · 27

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2020Language Models are Few-Shot Learnerscited by 32,419
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15 more
  1. 2018The Natural Language Decathlon: Multitask Learning as Question Answeringcited by 236
  2. 2019A Unified MRC Framework for Named Entity Recognitioncited by 199
  3. 2021How Many Data Points is a Prompt Worth?cited by 198
  4. 2020Event Extraction by Answering (Almost) Natural Questionscited by 177
  5. 2021Cutting Down on Prompts and Parameters: Simple Few-Shot Learning with Language Modelscited by 139
  6. 2019Deeper Text Understanding for IR with Contextual Neural Language Modelingcited by 113
  7. 2019Entity-Relation Extraction as Multi-Turn Question Answeringcited by 96
  8. 2019Coreference Resolution as Query-based Span Predictioncited by 91
  9. 2019Multi-Sentence Argument Linkingcited by 81
  10. 2019Dialog State Tracking: A Neural Reading Comprehension Approachcited by 62
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  12. 2019NLProlog: Reasoning with Weak Unification for Question Answering in Natural Languagecited by 37
  13. 2020Document-Level Event Role Filler Extraction using Multi-Granularity Contextualized Encodingcited by 28
  14. 2019Reading the Manual: Event Extraction as Definition Comprehensioncited by 26
  15. 2020Hierarchical Entity Typing via Multi-level Learning to Rankcited by 18

Built on by

Cited by year

Citing papers per year: 2022: 2; 2023: 1; 2024: 1.

4 papers.

  1. 2022On Event Individuation for Document-Level Information Extractioncited by 2
  2. 2023FAMuS: Frames Across Multiple Sourcescited by 2
  3. 2022Gradient Imitation Reinforcement Learning for General Low-Resource Information Extractioncited by 0
  4. 2024Small Models Are (Still) Effective Cross-Domain Argument Extractorscited by 0