Papers with relational graph convolutional network
Tracing Origins: Coreference-aware Machine Reading Comprehension (2022.acl-long)
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| Challenge: | a recent study has enriched pre-trained language models with syntactic, semantic and other linguistic information to improve their performance. |
| Approach: | They use a pre-trained language model to leverage coreference information to enhance word embeddings . they use additional encoder layers to focus on coreference mentions or a relational graph convolutional network to model the coreference relations. |
| Outcome: | The proposed model imitates the human reading process and leverages coreference information to enhance word embeddings. |
Graph-Based Knowledge Integration for Question Answering over Dialogue (2020.coling-main)
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| Challenge: | Existing approaches for question answering over dialogue did not consider dialogue structure and background knowledge (e.g., relationships between speakers). |
| Approach: | They propose a method which organizes a dialogue as a "relational graph" and uses edges to represent relationships between entities to encode multi-relations knowledge for reasoning. |
| Outcome: | The proposed method is better at tackling complex questions requiring relational reasoning and defending adversarial attacks with distracting sentences. |
A Novel Table-to-Graph Generation Approach for Document-Level Joint Entity and Relation Extraction (2023.acl-long)
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| Challenge: | Existing document-level relation extraction methods assume entities and their mentions are given beforehand, which is inadequate for real-world applications. |
| Approach: | They propose a table-to-graph generation model for joint extraction of entities and relations at document-level. |
| Outcome: | The proposed model surpasses existing methods by a large margin and achieves state-of-the-art results on a document-level relation extraction dataset. |