Papers with Graph Convolutional Network
Jointly Learning Entity and Relation Representations for Entity Alignment (D19-1)
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| Challenge: | Entity alignment is a viable method for integrating heterogeneous knowledge among different knowledge graphs (KGs). |
| Approach: | They propose a Graph Convolutional Network-based framework for learning relation representations by embedding relation seeds into entities and incorporating relation approximation into entities to iteratively improve alignment. |
| Outcome: | The proposed approach outperforms state-of-the-art methods on three real-world cross-lingual datasets. |
Graph Based Network with Contextualized Representations of Turns in Dialogue (2021.emnlp-main)
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| Challenge: | Dialogue-based relation extraction (RE) aims to extract relation(s) between two arguments that appear in a dialogue. |
| Approach: | They propose a dialogue-based relation extraction model which is based on emotion recognition in conversations. |
| Outcome: | The proposed model outperforms the state-of-the-art models on most of the benchmark datasets. |
Neural Collective Entity Linking (C18-1)
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| Challenge: | Entity linking aims to link entity mentions in texts to knowledge bases, but existing methods rely on local contexts to resolve entities independently. |
| Approach: | They propose a neural model for collective entity linking that integrates local contextual features and global coherence information to improve the computation efficiency. |
| Outcome: | The proposed model improves its performance on five publicly available datasets and can be used to train on Wikipedia hyperlinks to avoid overfitting and domain bias. |
Event Schema Induction with Double Graph Autoencoders (2022.naacl-main)
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| Challenge: | Experimental results show that a new method for learning event schemas from historical events is effective. |
| Approach: | They propose a new event schema induction framework which captures global dependencies among nodes in event graphs. |
| Outcome: | Experimental results show that the proposed model can learn event schemas with global consistency. |
Dating Documents using Graph Convolution Networks (P18-1)
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| Challenge: | Existing approaches for document dating assume accurate knowledge of document date, but this is not always available for arbitrary documents from the Web. |
| Approach: | They propose a Graph Convolutional Network (GCN) based document dating approach which exploits syntactic and temporal graph structures of document in a principled way. |
| Outcome: | The proposed approach outperforms state-of-the-art models on real-world datasets by 19% absolute accuracy points. |
Leveraging Argumentation Knowledge Graph for Interactive Argument Pair Identification (2021.findings-acl)
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| Challenge: | Existing researches focus on sentence matching but the interaction of opinions requires reasoning of knowledge, which is beyond textual information. |
| Approach: | They propose to leverage external knowledge to enhance the identification of interactive argument pairs by analyzing the discussion thread of the target topic in an online forum. |
| Outcome: | The proposed model achieves state-of-the-art in the benchmark dataset. |
MultiDM-GCN: Aspect-guided Response Generation in Multi-domain Multi-modal Dialogue System using Graph Convolutional Network (2020.findings-emnlp)
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| Challenge: | Existing research suggests that engaging conversations include visual cues (e.g., a video or images) or audio cue. |
| Approach: | They propose a multi-modal conversational framework that generates the responses following the different aspects of a product or service to cater to the user's needs. |
| Outcome: | The proposed framework outperforms baselines for the task-oriented dialogue setup. |
Bridging the Gap: Attending to Discontinuity in Identification of Multiword Expressions (N19-1)
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| Challenge: | Existing approaches to identify discontinuous multiword expressions are limited in dealing with discontinuous occurrences. |
| Approach: | They propose a method to tag Multiword Expressions using a language-independent deep learning architecture to target discontinuity. |
| Outcome: | The proposed model outperforms baseline models on a multilingual dataset and scores higher than baseline models. |
Multi-hop Question Generation with Graph Convolutional Network (2020.findings-emnlp)
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| Challenge: | Existing studies on text-based QG focus on generating SQuAD-style questions. |
| Approach: | They propose a multi-hop question generation model that does context encoding in multiple hops with Graph Convolutional Network and encoder fusion via an Encoder Reasoning Gate. |
| Outcome: | Empirical results show that the proposed model generates fluent questions with high completeness and outperforms baselines on automatic evaluation metrics. |
Adaptive Graph Convolutional Network for Knowledge Graph Entity Alignment (2022.findings-emnlp)
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| Challenge: | Entity alignment (EA) aims to identify equivalent entities from different Knowledge Graphs (KGs) noisy neighbors of entities transfer invalid information, drown out equivalent information, and ultimately reduce the performance of EA. |
| Approach: | They propose a method to deal with neighbor noises to reduce the performance of EA by capturing the differences and complementarities of multiple KGs. |
| Outcome: | The proposed framework outperforms the state-of-the-art methods in supervised and unsupervised settings. |
Aspect-based Sentiment Classification with Aspect-specific Graph Convolutional Networks (D19-1)
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| Challenge: | Existing aspects-based sentiment classification models lack a mechanism to account for relevant syntactical constraints and word dependencies. |
| Approach: | They propose to build a Graph Convolutional Network over the dependency tree of a sentence to exploit syntactical information and word dependencies. |
| Outcome: | The proposed model is comparable to state-of-the-art models on three benchmarking collections. |
BERT4GCN: Using BERT Intermediate Layers to Augment GCN for Aspect-based Sentiment Classification (2021.emnlp-main)
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| Challenge: | Existing approaches to Aspect-based sentiment classification ignore sequential features of context and lack syntactic knowledge of sentences. |
| Approach: | They propose a model which integrates sequential grammatical features from context and syntactic knowledge from dependency graphs to augment GCN to better encode dependency graph outputs. |
| Outcome: | The proposed model outperforms state-of-the-art models when equipped with contextual word embedding from pre-training language models. |
Bidirectional Skeleton-Based Isolated Sign Recognition using Graph Convolutional Networks (2022.lrec-1)
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| Challenge: | a new method for computer-based sign recognition from video is proposed . it involves explicit detection of the start and end frames of isolated signs . |
| Approach: | They propose a skeleton-based method that involves explicit detection of start and end frames of signs . they apply a modified WLASL dataset with corrections to the gloss labeling . |
| Outcome: | The proposed method outperforms state-of-the-art methods on the modified WLASL dataset . it has a success rate of 77.43% and 94.54% for top-5 . |