Tree-Constrained Graph Neural Networks For Argument Mining
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Abstract (arXiv)
We propose a novel architecture for Graph Neural Networks that is inspired by the idea behind Tree Kernels of measuring similarity between trees by taking into account their common substructures, named fragments. By imposing a series of regularization constraints to the learning problem, we exploit a pooling mechanism that incorporates such notion of fragments within the node soft assignment function that produces the embeddings. We present an extensive experimental evaluation on a collection of sentence classification tasks conducted on several argument mining corpora, showing that the proposed approach performs well with respect to state-of-the-art techniques.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 26 of its 42 references that are in the map. First posted on arXiv 2 Sep 2021.
Builds on
26 of 42 references.
References · 26
- 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
- 2017Graph Attention Networkscited by 8,854
- 2017Inductive Representation Learning on Large Graphscited by 6,284
- 2018How Powerful are Graph Neural Networks?cited by 4,355
- 2017Neural Message Passing for Quantum Chemistrycited by 3,460
- 2019A Comprehensive Survey on Graph Neural Networkscited by 2,906
- 2018Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networkscited by 878
- 2018Hierarchical Graph Representation Learning with Differentiable Poolingcited by 786
- 2015Convolutional Networks on Graphs for Learning Molecular Fingerprintscited by 751
- 2019Self-Attention Graph Poolingcited by 421
- 2019DAG-GNN: DAG Structure Learning with Graph Neural Networkscited by 329
- 2015Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networkscited by 305
14 more
- 2019Attention in Natural Language Processingcited by 100
- 2016Parsing Argumentation Structures in Persuasive Essayscited by 99
- 2018Tree-to-tree Neural Networks for Program Translationcited by 93
- 2019Hierarchical Graph Pooling with Structure Learningcited by 81
- 2020Rethinking pooling in graph neural networkscited by 73
- 2020Lagrangian Duality for Constrained Deep Learningcited by 72
- 2018Cross-topic Argument Mining from Heterogeneous Sources Using Attention-based Neural Networkscited by 58
- 2019Classification and Clustering of Arguments with Contextualized Word Embeddingscited by 57
- 2020Second-Order Pooling for Graph Neural Networkscited by 33
- 2021Neural Trees for Learning on Graphscited by 13
- 2020Tree Structure-Aware Graph Representation Learning via Integrated Hierarchical Aggregation and Relational Metric Learningcited by 11
- 2021Multi-Task Attentive Residual Networks for Argument Miningcited by 6
- 2013Optimal Junction Treescited by 4
- 2020Learning from Non-Binary Constituency Trees via Tensor Decompositioncited by 3
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Citing papers per year: 2025: 1; 2026: 1.
2 papers.