| Challenge: | Existing OIE systems split a sentence into tokens and recognize token spans as tuple relations and arguments. |
| Approach: | They propose to split a sentence into tokens and recognize token spans as tuple relations and arguments. |
| Outcome: | The proposed model achieves state-of-the-art on multiple OIE datasets showing that SaC has better properties than sentence as token sequence. |
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LSOIE: A Large-Scale Dataset for Supervised Open Information Extraction (2021.eacl-main)
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| Challenge: | Open Information Extraction (OIE) systems extract factual propositions into n-ary tuples . current datasets are limited in size and diversity . |
| Approach: | They propose to convert QA-SRL 2.0 dataset to large-scale OIE dataset LSOIE. |
| Outcome: | The proposed dataset is 20 times larger than the next largest human-annotated OIE dataset. |
Syntactic Multi-view Learning for Open Information Extraction (2022.emnlp-main)
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| Challenge: | Open Information Extraction (OpenIE) aims to generate structured tuples from unstructured open-domain text. |
| Approach: | They propose to model constituency and dependency trees into word-level graphs and combine them with sentential semantic representations to extract relational tuples. |
| Outcome: | The proposed model integrates constituency and dependency trees into word-level graphs and enables neural OpenIE to learn from syntactic structures. |
Syntactically Rich Discriminative Training: An Effective Method for Open Information Extraction (2022.emnlp-main)
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| Challenge: | Open information extraction (OIE) is the task of extracting facts from natural language text. |
| Approach: | They propose a method for computing syntactically rich text embeddings using the structure of dependency trees and a discriminative approach to OIE where tokens in the generated fact are classified as "real" and "fake" they propose to reduce repetitive tokens and improve models' ability to generate implicit facts by a factor of 23%. |
| Outcome: | The proposed method reduces repetitive tokens by a factor of 23% on the CaRB, OIE2016, and LSOIE datasets and improves on augmented datasets. |
Open Information Extraction from Question-Answer Pairs (N19-1)
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| Challenge: | Existing work on OpenIE extracts structured data from sentences . a system for extracting tuples from question-answer pairs solves this problem . |
| Approach: | They propose a system for extracting tuples from question-answer pairs . they use distributed representations of a question and an answer to generate knowledge facts . |
| Outcome: | The proposed system extracts meaningful structured tuples from question-answer pairs . it can find new and interesting facts to extend knowledge bases, the authors show . |
OIE@OIA: an Adaptable and Efficient Open Information Extraction Framework (2022.acl-long)
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| Challenge: | Different Open Information Extraction (OIE) tasks require different types of information. |
| Approach: | They propose to adapt an OIE Graph to different OIE tasks with simple rules . they implement an end-to-end OIA generator and make it open-accessible . |
| Outcome: | The proposed system achieves new SOTA performance on three popular OIE tasks. |
MILIE: Modular & Iterative Multilingual Open Information Extraction (2022.acl-long)
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Bhushan Kotnis, Kiril Gashteovski, Daniel Rubio, Ammar Shaker, Vanesa Rodriguez-Tembras, Makoto Takamoto, Mathias Niepert, Carolin Lawrence
| Challenge: | Current OpenIE systems extract all triple slots independently. |
| Approach: | They propose a neural OpenIE system that extracts triple slots iteratively . they propose to use the system to extract easy slots and difficult ones . |
| Outcome: | The proposed system outperforms SOTA systems on multiple languages ranging from Chinese to Arabic. |
Open Information Extraction with Entity Focused Constraints (2023.findings-eacl)
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| Challenge: | Open Information Extraction (OIE) is the task of extracting tuples from unstructured corpora without any knowledge of the type and lexical form of the subject, the object, or the subject. |
| Approach: | They exploit domain knowledge to inject constraints into the extraction through constrained inference and constraint-aware training. |
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Leveraging Linguistically Enhanced Embeddings for Open Information Extraction (2024.lrec-main)
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| Challenge: | Open Information Extraction (OIE) is a structure prediction task in NLP that aims to extract structured n-ary tuples from free text. |
| Approach: | They propose to leverage linguistic features with a Seq2Seq PLM for OIE to improve performance. |
| Outcome: | The proposed methods give any neural OIE architecture the key performance boost from both PLMs and linguistic features in one go. |
DocOIE: A Document-level Context-Aware Dataset for OpenIE (2021.findings-acl)
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| Challenge: | Existing solutions focus on extracting tuples at sentence level, but sentences exist as part of a document rather than standalone. |
| Approach: | They propose to annotate 800 sentences from 80 documents to form a DocOIE dataset . they propose to use document-level context to improve OpenIE performance . |
| Outcome: | The proposed OpenIE model improves performance by incorporating documentlevel context into the dataset. |
Open Information Extraction on Scientific Text: An Evaluation (C18-1)
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| Challenge: | Open Information Extraction (OIE) is the unsupervised creation of structured information from text. |
| Approach: | They propose to use two state-of-the-art OIE systems to evaluate the performance of OIE on scientific texts originating from 10 different disciplines. |
| Outcome: | The proposed methods perform significantly worse on scientific text than encyclopedic text. |