| Challenge: | Existing approaches to open information extraction only work with unrealistically small numbers of entities and relations. |
| Approach: | They propose to use a transformer encoder-decoder model to extract triplets from unstructured text . they use 'generative information extraction' to generate triplet representations of information . |
| Outcome: | The proposed model is state-of-the-art on closed information extraction and generalizes from fewer training data points than baselines. |
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| Challenge: | Existing approaches to extract relation triplets from text often involve multiple-step pipelines that propagate errors or are limited to a small number of relation types. |
| Approach: | They propose to use autoregressive seq2seq models to simplify Relation Extraction by expressing triplets as a sequence of text and a model that performs end-to-end relation extraction for more than 200 different relation types. |
| Outcome: | The proposed model achieves state-of-the-art on an array of Relation Extraction and Relation Classification benchmarks and achieves top performance in most of them. |
Cost-effective End-to-end Information Extraction for Semi-structured Document Images (2021.emnlp-main)
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| Challenge: | a real-world information extraction system for semi-structured document images often involves a long pipeline of multiple modules, which can lead to unstable performance if not designed carefully. |
| Approach: | They propose to use a sequence generation task to build an end-to-end IE system . they propose to combine three manually engineered modules with one data-driven module . |
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OpenKI: Integrating Open Information Extraction and Knowledge Bases with Relation Inference (N19-1)
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| Challenge: | Existing methods for knowledge extraction and alignment are limited in quality and performance. |
| Approach: | They propose to integrate OpenIE extractions in the form of (subject, predicate, object) triples with Knowledge Bases (KB) |
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A Survey of Generative Information Extraction (2025.coling-main)
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| Challenge: | Information Extraction (IE) is a popular and fundamental task in natural language processing. |
| Approach: | They first review generative information extraction methods based on pre-trained language models and large language models focusing on their adaptation and generalization capabilities. |
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DORE: Document Ordered Relation Extraction based on Generative Framework (2022.findings-emnlp)
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| Challenge: | Existing generative methods do not fit document-level relation extraction tasks where there are multiple entities and relational facts. |
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Document-level Entity-based Extraction as Template Generation (2021.emnlp-main)
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| Challenge: | Document-level entity-based extraction (EE) tasks extract entity-centric information from unstructured text across multiple sentences. |
| Approach: | They propose a generative framework for two document-level EE tasks: role-filler entity extraction (RE) and relation extraction ( RE). |
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Autoregressive Entity Generation for End-to-End Task-Oriented Dialog (2022.coling-1)
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| Challenge: | Task-oriented dialog systems require external knowledge base to generate a response . current systems require scanning the KB at each turn, which is inefficient when the kb scales up . |
| Approach: | They propose to generate entity autoregressively before leveraging it to guide response generation. |
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WebIE: Faithful and Robust Information Extraction on the Web (2023.acl-long)
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| Challenge: | Existing closed IE datasets are built using Wikipedia, but they have limitations when applied to web domains. |
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A Frustratingly Easy Approach for Entity and Relation Extraction (2021.naacl-main)
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| Challenge: | Existing work on end-to-end relation extraction models combine two tasks: named entity recognition and relation extraction. |
| Approach: | They propose a pipelined approach for entity and relation extraction that uses two independent encoders to construct the relation model. |
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Learning to Extract Structured Entities Using Language Models (2024.emnlp-main)
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| Challenge: | Language Models (LMs) play a pivotal role in extracting structured information from unstructured text. |
| Approach: | They propose to reformulate the task to be entity-centric, enabling the use of diverse metrics that can provide more insights from various perspectives. |
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