Papers with joint extraction
TPLinker: Single-stage Joint Extraction of Entities and Relations Through Token Pair Linking (2020.coling-main)
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| Challenge: | Existing methods to extract entities and relations from unstructured text are susceptible to cascading errors due to the separation of entity detection and relation classification. |
| Approach: | They propose a one-stage joint extraction model that detects overlapping relations while being immune from exposure bias. |
| Outcome: | The proposed model can identify overlapping relations while being immune from exposure bias. |
Extracting Entities and Relations with Joint Minimum Risk Training (D18-1)
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| Challenge: | Existing methods for detecting entities and relations are limited by the complexity of the joint learning paradigm. |
| Approach: | They propose a joint learning paradigm based on minimum risk training . they implement a strong and simple neural network to execute the MRT . |
| Outcome: | The proposed model is able to achieve state-of-the-art in the extraction task on ACE05 and NYT datasets. |
ENPAR:Enhancing Entity and Entity Pair Representations for Joint Entity Relation Extraction (2021.eacl-main)
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| Challenge: | Existing methods for joint entity relation extraction use multitask learning frameworks, but annotations for additional tasks are hard to obtain. |
| Approach: | They propose a pre-training method to improve the joint extraction performance with just extra entity annotations. |
| Outcome: | The proposed method outperforms existing methods on ACE05, SciERC, and NYT and outperformed BERT on other tasks. |
Neural Extractive Text Summarization with Syntactic Compression (D19-1)
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| Challenge: | Recent approaches to summarization are either selection-based extraction or generation-based abstraction. |
| Approach: | They propose a neural model for single-document summarization based on joint extraction and syntactic compression. |
| Outcome: | The proposed model outperforms an off-the-shelf compression module and its output generally remains grammatical. |
Exploring Layer Activation Dynamic of CoT via Knowledge Probe (2026.acl-long)
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| Challenge: | Chain-of-thought reasoning has emerged as a crucial paradigm for multi-step reasoning tasks. |
| Approach: | They propose a multi-stage probing framework that enforces structured reasoning with three explicit stages: keyword extraction, theorem generation, and computation execution. |
| Outcome: | The proposed framework enforces structured reasoning with three explicit stages: keyword extraction, theorem generation, and computation execution. |
Distantly-Supervised Joint Extraction with Noise-Robust Learning (2024.findings-acl)
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| Challenge: | Existing approaches to identifying entity pairs and relations with a single model are noisy . Existing methods only consider one source of noise or make decisions using external knowledge . |
| Approach: | They propose a framework that aligns entity mentions with corresponding tags for joint extraction . they propose DENRL, which employs a lightweight transformer backbone for joint tagging . |
| Outcome: | The proposed framework outperforms baseline models on two benchmark datasets with better interpretability. |
MEGClass: Extremely Weakly Supervised Text Classification via Mutually-Enhancing Text Granularities (2023.findings-emnlp)
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| Challenge: | Existing methods for text classification use human annotations or a set of class seed words for supervision, which can be costly, especially in emerging domains. |
| Approach: | They propose a weakly-supervised method that leverages mutually-enhancing text granularities to learn a contextualized document representation that captures the most discriminative class indicators. |
| Outcome: | Extensive experiments on seven benchmark datasets show that MEGClass outperforms other weakly and extremely weakly supervised methods. |