Learning Reasoning Patterns for Relational Triple Extraction with Mutual Generation of Text and Graph (2022.findings-acl)
Copied to clipboard
| Challenge: | Existing methods focused on learning text patterns from explicit mentions but failed to extract the implicitly implied triples. |
| Approach: | They propose to construct a relational graph from a sentence and apply multi-layer graph convolutions to capture the type inference logic of the paths. |
| Outcome: | The proposed framework can find multi-hop reasoning paths and capture type inference logic with the sentence's supplementary relational expressions. |
Similar Papers
Jointly Extracting Explicit and Implicit Relational Triples with Reasoning Pattern Enhanced Binary Pointer Network (2021.naacl-main)
Copied to clipboard
| Challenge: | Existing methods for relational triple extraction ignore implicit triples that lack explicit expressions, leading to incomplete knowledge graphs. |
| Approach: | They propose a binary pointer network to extract explicit and implicit relational triples from sentences and to retain the information of extracted triples in an external memory. |
| Outcome: | The proposed framework extracts overlapping triples relevant to each word sequentially and retains the information of extracted triples in an external memory. |
RelU-Net: Syntax-aware Graph U-Net for Relational Triple Extraction (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods focused on capturing semantic information but failed to incorporate syntactic structures of the sentence, which is proved to contain rich relational information. |
| Approach: | They propose a framework to capture syntactic information for relational triple extraction by contracting dependency tree into a core relational topology and eliminating redundant information with graph pooling operations. |
| Outcome: | The proposed framework incorporates syntactic information for relational triple extraction. |
A Novel Cascade Binary Tagging Framework for Relational Triple Extraction (2020.acl-main)
Copied to clipboard
| Challenge: | Existing approaches to extract relational triples from unstructured text are inadequate to solve the overlapping triple problem. |
| Approach: | They propose a cascade binary tagging framework that models relations as functions that map subjects to objects in a sentence. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on two datasets . it outperformed baseline methods by 17.5 and 30.2 absolute gains . |
Learning to Map Natural Language Statements into Knowledge Base Representations for Knowledge Base Construction (L18-1)
Copied to clipboard
| Challenge: | Currently, the construction and updating of knowledge bases rely on human labor. |
| Approach: | They propose to map relational phrases in triples from natural language to knowledge base predicate format. |
| Outcome: | The proposed mapping results show high quality and promising coverage on relational phrases compared to previous research. |
StereoRel: Relational Triple Extraction from a Stereoscopic Perspective (2021.acl-long)
Copied to clipboard
| Challenge: | Existing methods for relational triple extraction still face challenges, including information loss and error propagation. |
| Approach: | They propose a model which maps relational triples to a three-dimensional space and leverages three decoders to extract them. |
| Outcome: | The proposed model outperforms the baselines on five public datasets. |
Improving Recall of Large Language Models: A Model Collaboration Approach for Relational Triple Extraction (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing large language models can extract triples from simple sentences with few-shot learning or fine-tuning, but they often miss out when extracting from complex sentences. |
| Approach: | They propose an evaluation-filtering framework that integrates large language models with small models for relational triple extraction tasks. |
| Outcome: | The proposed framework integrates large language models with small models for relational triple extraction tasks. |
EmRel: Joint Representation of Entities and Embedded Relations for Multi-triple Extraction (2022.naacl-main)
Copied to clipboard
| Challenge: | Existing studies only explore entity representations, but propose a novel triple perspective for relation extraction. |
| Approach: | They propose to explicitly introduce relation representation and jointly represent it with entities to identify valid triples. |
| Outcome: | The proposed method is based on ablations and document-level relation extraction and joint entity and relation extraction. |
Language Generation with Multi-Hop Reasoning on Commonsense Knowledge Graph (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches that integrate commonsense knowledge into pre-trained language models simply transfer relational knowledge while ignoring rich connections within the knowledge graph. |
| Approach: | They propose a method that leverages structural and semantic information of the knowledge graph to generate commonsense-aware text. |
| Outcome: | The proposed method outperforms baseline models on three text generation tasks that require reasoning over commonsense knowledge. |
Adjacency List Oriented Relational Fact Extraction via Adaptive Multi-task Learning (2021.findings-acl)
Copied to clipboard
| Challenge: | Existing models for relational fact extraction do not analyze the output data structure from the perspective of graph representation flexibility and heterogeneity. |
| Approach: | They propose a relational fact extraction model based on graph-oriented analytical perspective that outperforms other models. |
| Outcome: | The proposed model outperforms state-of-the-art models on two benchmark datasets and shows that it is flexible and space-efficient. |
Query-based Instance Discrimination Network for Relational Triple Extraction (2022.emnlp-main)
Copied to clipboard
| Challenge: | Recent approaches to extract relational triples from open domain texts suffer from error propagation, relation redundancy and lack of high-level connections. |
| Approach: | They propose a query-based approach to construct instance-level representations for relational triples . they use query embeddings and token embeddables to extract all types of triples in one step . |
| Outcome: | The proposed method achieves state-of-the-art on five widely used benchmarks. |