Challenge: et al., 2017) focus on visual features individually, while ignoring relationship information among image features that provides important guidance for generating sentences.
Approach: They propose a joint relationship attention network that explores the relationships among image features.
Outcome: The proposed method achieves state-of-the-art performance on large-scale datasets and on Flickr30k datasets.

Similar Papers

Improving Distantly-Supervised Relation Extraction with Joint Label Embedding (D19-1)

Copied to clipboard

Challenge: Existing methods for relation extraction treat labels as independent and meaningless one-hot vectors, which cause a loss of potential label information for selecting valid instances.
Approach: They propose a multi-layer attention-based model to improve relation extraction with joint label embedding by gating integration and using the embeddable entities as an atten- tion.
Outcome: The proposed model significantly outperforms state-of-the-art methods in relation extraction with joint label embedding.
Neural Data-to-Text Generation via Jointly Learning the Segmentation and Correspondence (2020.acl-main)

Copied to clipboard

Challenge: Recent neural attention models conflate all steps into a single end-to-end system and simplify training process.
Approach: They propose to explicitly segment target text into fragment units and align them with their data correspondences.
Outcome: The proposed model outperforms neural attention models on E2E and WebNLG benchmarks.
CARE: Co-Attention Network for Joint Entity and Relation Extraction (2024.lrec-main)

Copied to clipboard

Challenge: Existing joint entity and relation extraction methods suffer from feature confusion or inadequate interaction between the two subtasks.
Approach: They propose a Co-Attention network for joint entity and relation extraction that adopts a parallel encoding strategy to learn separate representations for each subtask.
Outcome: The proposed model outperforms existing models on three datasets . it uses a parallel encoding strategy to learn separate representations for each subtask .
Joint Multi-Label Attention Networks for Social Text Annotation (N19-1)

Copied to clipboard

Challenge: Present research shows that title metadata could affect social annotation.
Approach: They propose a title-guided attention network for document annotation with user-generated tags that separates the title from the content of a document and applies a semantic-based loss regulariser over each sentence in the content.
Outcome: The proposed approach outperforms the Bi-GRU and Hierarchical Attention Network (HAN) on two open datasets with 10%-30% reduction in training time.
Synchronous Dual Network with Cross-Type Attention for Joint Entity and Relation Extraction (2021.emnlp-main)

Copied to clipboard

Challenge: Existing studies on joint entity and relation extraction fail to fully utilize the interdependence between entity types and relation types.
Approach: They propose a synchronous dual network with cross-type attention via separately and interactively considering the entity types and relation types.
Outcome: The proposed model achieves state-of-the-art on NYT and WebNLG datasets.
Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs (P19-1)

Copied to clipboard

Challenge: Existing knowledge graphs (KGs) are incomplete or partial information, in the form of missing relations between entities, which gives rise to the task of knowledge base completion (also known as relation prediction).
Approach: They propose to capture both entity and relation features in any given neighborhood and encapsulate relation clusters and multi-hop relations in their attention-based model.
Outcome: The proposed model captures both entity and relation features in any given neighborhood and also encapsulates relation clusters and multi-hop relations.
Relation Classification Using Segment-Level Attention-based CNN and Dependency-based RNN (N19-1)

Copied to clipboard

Challenge: Recent work on relation classification has gained much success by exploiting deep neural networks.
Approach: They propose a relation classification model using Segment-level Attention-based Convolutional Neural Networks and Dependency-based Recurrent Neural networks.
Outcome: The proposed model is comparable to the state-of-the-art without external lexical features on the SemEval-2010 dataset.
Span-based Joint Entity and Relation Extraction with Attention-based Span-specific and Contextual Semantic Representations (2020.coling-main)

Copied to clipboard

Challenge: Existing methods treat each span token equally important, ignoring significant features.
Approach: They propose a span-based joint extraction framework with attention-based semantic representations that utilizes span-specific and contextual representations.
Outcome: The proposed model outperforms existing models on ACE2005, CoNLL2004 and ADE.
Synergetic Interaction Network with Cross-task Attention for Joint Relational Triple Extraction (2024.lrec-main)

Copied to clipboard

Challenge: Existing approaches to joint entity-relation extraction are limited in their ability to capture the interdependence between the two sub-tasks.
Approach: They propose a synergistic approach to capture interdependence between named entity recognition and relation extraction sub-tasks in a Synergetic Interaction Network.
Outcome: The proposed model achieves significantly better performance on three benchmark datasets.
Learning to Relate from Captions and Bounding Boxes (P19-1)

Copied to clipboard

Challenge: Existing methods for classifying images without supervision are limited.
Approach: They propose a top-down attention mechanism to align entities in captions to objects in the image and leverage the syntactic structure of captions for alignment.
Outcome: The proposed model achieves a recall@50 of 15% and recall@100 of 25% on the relationships present in the image and predicts relations that are not present in captions.

What is GenGO?

GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

Information

About
Limitations