mlsanityData to 14 Oct 2026

Iterative Document-level Information Extraction via Imitation Learning

arXiv 2210.06600 ↗First posted 12 Oct 2022cs.CLcited by 11

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Abstract (arXiv)

We present a novel iterative extraction model, IterX, for extracting complex relations, or templates (i.e., N-tuples representing a mapping from named slots to spans of text) within a document. Documents may feature zero or more instances of a template of any given type, and the task of template extraction entails identifying the templates in a document and extracting each template's slot values. Our imitation learning approach casts the problem as a Markov decision process (MDP), and relieves the need to use predefined template orders to train an extractor. It leads to state-of-the-art results on two established benchmarks -- 4-ary relation extraction on SciREX and template extraction on MUC-4 -- as well as a strong baseline on the new BETTER Granular task.

Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). Builds on 22 of its 59 references that are in the map. First posted on arXiv 12 Oct 2022.

Builds on

22 of 59 references.

References · 22

  1. 2017Attention Is All You Needcited by 60,361
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  3. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  4. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
  5. 2014On the Properties of Neural Machine Translation: Encoder-Decoder Approachescited by 1,304
  6. 2015Scheduled Sampling for Sequence Prediction with Recurrent Neural Networkscited by 803
  7. 2018AllenNLP: A Deep Semantic Natural Language Processing Platformcited by 347
  8. 2016Bidirectional Attention Flow for Machine Comprehensioncited by 278
  9. 2017End-to-end Neural Coreference Resolutioncited by 241
  10. 2019DocRED: A Large-Scale Document-Level Relation Extraction Datasetcited by 239
  11. 2019Levenshtein Transformercited by 199
  12. 2021Document-Level Event Argument Extraction by Conditional Generationcited by 155
10 more
  1. 2020SciREX: A Challenge Dataset for Document-Level Information Extractioncited by 113
  2. 2017Cross-Sentence N-ary Relation Extraction with Graph LSTMscited by 101
  3. 2019Multi-Sentence Argument Linkingcited by 81
  4. 2016Distant Supervision for Relation Extraction beyond the Sentence Boundarycited by 62
  5. 2015Learning to Search Better Than Your Teachercited by 50
  6. 2021Document-level Entity-based Extraction as Template Generationcited by 33
  7. 2021LOME: Large Ontology Multilingual Extractioncited by 23
  8. 2021Everything Is All It Takes: A Multipronged Strategy for Zero-Shot Cross-Lingual Information Extractioncited by 16
  9. 2018A Multi-sentiment-resource Enhanced Attention Network for Sentiment Classificationcited by 4
  10. 2019Improving Long Distance Slot Carryover in Spoken Dialogue Systemscited by 4

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 1; 2023: 3; 2024: 5; 2025: 1; 2026: 1.

11 papers.

  1. 2023A Comprehensive Survey on Relation Extraction: Recent Advances and New Frontierscited by 22
  2. 2025WikiVideo: Article Generation from Multiple Videoscited by 13
  3. 2023A Unified View of Evaluation Metrics for Structured Predictioncited by 11
  4. 2024Grounding Partially-Defined Events in Multimodal Datacited by 10
  5. 2024A Survey of Video Datasets for Grounded Event Understandingcited by 9
  6. 2024MultiMUC: Multilingual Template Filling on MUC-4cited by 3
  7. 2022On Event Individuation for Document-Level Information Extractioncited by 2
  8. 2023FAMuS: Frames Across Multiple Sourcescited by 2
  9. 2024Event-Keyed Summarizationcited by 2
  10. 2024Are Triggers Needed for Document-Level Event Extraction?cited by 1
  11. 2026Object Aligner: A Configurable JSON Schema Similarity Score for Graphs, Applied to LLM Prompt Optimizationcited by 0