Incorporating Syntax and Semantics in Coreference Resolution with Heterogeneous Graph Attention Network (2021.naacl-main)
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| Challenge: | Existing neural coreference resolution models lack syntactic and semantic information . however, such information has been shown to benefit other tasks. |
| Approach: | They propose a graph-based model that incorporates syntactic and semantic structures of sentences. |
| Outcome: | The proposed model incorporates syntactic and semantic structures of sentences. |
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| Challenge: | Aspect-based Sentiment Analysis (ABSA) aims to predict sentiment polarity towards aspects in sentences . a novel model for ABSA is proposed, but how to harness it is still a challenge . |
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| Challenge: | Existing graph-based approaches to learn static structures and dynamic latent trees are lacking in incorporating semantic and syntactic information simultaneously within complex global structures. |
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| Challenge: | Extensive research efforts have been devoted to the task of matching two natural language sentences. |
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Enhancing Unrestricted Cross-Document Event Coreference with Graph Reconstruction Networks (2024.lrec-main)
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| Challenge: | Event Coreference Resolution is a discourse-oriented task that requires a lot of computational power. |
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Incorporating Syntactic and Semantic Information in Word Embeddings using Graph Convolutional Networks (P19-1)
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Shikhar Vashishth, Manik Bhandari, Prateek Yadav, Piyush Rai, Chiranjib Bhattacharyya, Partha Talukdar
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Graph Enhanced Dual Attention Network for Document-Level Relation Extraction (2020.coling-main)
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| Challenge: | Document-level relation extraction requires inter-sentence reasoning capabilities to capture local and global contextual information for multiple relation facts. |
| Approach: | They propose to characterize the interaction between sentences and potential relation instances via a Graph Enhanced Dual Attention network (GEDA) . they also propose a simple yet effective regularizer based on the natural duality of the S2R and R2S attentions, whose weights are also supervised by the supporting evidence of relation instances during training. |
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Syntax-Aware Graph Attention Network for Aspect-Level Sentiment Classification (2020.coling-main)
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| Challenge: | Existing approaches to aspect-level sentiment classification focus on modeling the relationship between aspect words and their contexts with attention, and ignore the use of elaborate knowledge implicit in the context. |
| Approach: | They exploit syntactic awareness to the model by the graph attention network on the dependency tree structure and external pre-training knowledge by BERT language model, which helps to model the interaction between the context and aspect words better. |
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