Papers with graph-based neural models
High-order Refining for End-to-end Chinese Semantic Role Labeling (2020.aacl-main)
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| Challenge: | Current semantic role labeling methods are limited to short-term features and local decisions. |
| Approach: | They propose a high-order refining mechanism to perform interaction between all predicate-argument pairs via attention calculation. |
| Outcome: | The proposed model achieves state-of-the-art on Chinese SRL data, including CoNLL09 and Universal Proposition Bank, while relieving the long-range dependency issues. |
Improving Graph-Based Text Representations with Character and Word Level N-grams (2022.aacl-short)
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| Challenge: | Graph-based text representation is important in downstream natural language processing tasks. |
| Approach: | They propose a heterogeneous word-character text graph that combines word and character n-gram nodes together with document nodes. |
| Outcome: | The proposed graph outperforms baselines and state-of-the-art models in text classification and automatic summarization. |
Directed Acyclic Graph Network for Conversational Emotion Recognition (2021.acl-long)
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| Challenge: | Empirical evidence shows that a good representation of conversation context significantly contributes to the model performance. |
| Approach: | They propose to encode query utterances with a directed acyclic graph to better model the intrinsic structure within a conversation. |
| Outcome: | The proposed model outperforms existing models on four ERC benchmarks with state-of-the-art models employed as baselines. |
Connecting the Dots: Document-level Neural Relation Extraction with Edge-oriented Graphs (D19-1)
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| Challenge: | Existing approaches to document-level relation extraction use nodes and edges as relations between nodes. |
| Approach: | They propose an edge-oriented graph neural model for document-level relation extraction that uses different types of nodes and edges to create a document-based graph. |
| Outcome: | The proposed model can learn intra- and inter-sentence relations using multi-instance learning internally. |