| Challenge: | Existing methods for matching sentence pairs do not perform well in longer documents . Existing approaches for matching sentences do not work in longer document understanding tasks . |
| Approach: | They propose to model article pairs by comparing sentences that enclose same concept vertex . they propose to use a concept interaction graph to match articles by encoding sentences . |
| Outcome: | The proposed methods show significant improvements over existing methods . the proposed datasets consist of 30K pairs of breaking news articles . |
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Graph Convolutional Networks for Event Causality Identification with Rich Document-level Structures (2021.naacl-main)
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Semantic Linking in Convolutional Neural Networks for Answer Sentence Selection (D18-1)
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Convolutional Interaction Network for Natural Language Inference (D18-1)
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Inter-sentence Relation Extraction with Document-level Graph Convolutional Neural Network (P19-1)
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Leveraging Argumentation Knowledge Graph for Interactive Argument Pair Identification (2021.findings-acl)
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Neural Graph Matching Networks for Chinese Short Text Matching (2020.acl-main)
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Joint Type Inference on Entities and Relations via Graph Convolutional Networks (P19-1)
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| Challenge: | a novel graph convolutional network (GCN) is proposed for the task of joint entity relation extraction. |
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Semantic Sentence Matching via Interacting Syntax Graphs (2022.coling-1)
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| Challenge: | Extensive research efforts have been devoted to the task of matching two natural language sentences. |
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Recognizing Semantic Relations by Combining Transformers and Fully Connected Models (2020.lrec-1)
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| Challenge: | Current approaches to recognizing semantic relations between words are limited and require a word-path model. |
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