Knowledge Graph for NLG in the context of conversational agents
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Abstract (arXiv)
The use of knowledge graphs (KGs) enhances the accuracy and comprehensiveness of the responses provided by a conversational agent. While generating answers during conversations consists in generating text from these KGs, it is still regarded as a challenging task that has gained significant attention in recent years. In this document, we provide a review of different architectures used for knowledge graph-to-text generation including: Graph Neural Networks, the Graph Transformer, and linearization with seq2seq models. We discuss the advantages and limitations of each architecture and conclude that the choice of architecture will depend on the specific requirements of the task at hand. We also highlight the importance of considering constraints such as execution time and model validity, particularly in the context of conversational agents. Based on these constraints and the availability of labeled data for the domains of DAVI, we choose to use seq2seq Transformer-based models (PLMs) for the Knowledge Graph-to-Text Generation task. We aim to refine benchmark datasets of kg-to-text generation on PLMs and to explore the emotional and multilingual dimensions in our future work. Overall, this review provides insights into the different approaches for knowledge graph-to-text generation and outlines future directions for research in this area.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 41 of its 59 references that are in the map. First posted on arXiv 4 Jul 2023.
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References · 41
- 2017Attention Is All You Needcited by 59,932
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
- 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
- 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,874
- 2015Gated Graph Sequence Neural Networkscited by 792
- 2020A Generalization of Transformer Networks to Graphscited by 653
- 2020Knowledge Graphscited by 619
- 2019ERNIE: Enhanced Language Representation with Informative Entitiescited by 525
- 2019K-BERT: Enabling Language Representation with Knowledge Graphcited by 303
- 2019Knowledge Enhanced Contextual Word Representationscited by 294
- 2020FEQA: A Question Answering Evaluation Framework for Faithfulness Assessment in Abstractive Summarizationcited by 292
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- 2019TuckER: Tensor Factorization for Knowledge Graph Completioncited by 290
- 2017Challenges in Data-to-Document Generationcited by 226
- 2021On Hallucination and Predictive Uncertainty in Conditional Language Generationcited by 176
- 2019Text Generation from Knowledge Graphs with Graph Transformerscited by 122
- 2020Investigating Pretrained Language Models for Graph-to-Text Generationcited by 117
- 2018Data-to-Text Generation with Content Selection and Planningcited by 106
- 2020Text-to-Text Pre-Training for Data-to-Text Taskscited by 104
- 2021Pretrained Language Models for Text Generation: A Surveycited by 95
- 2020Controlled Hallucinations: Learning to Generate Faithfully from Noisy Datacited by 83
- 2019Graph Transformer for Graph-to-Sequence Learningcited by 82
- 2019Step-by-Step: Separating Planning from Realization in Neural Data-to-Text Generationcited by 75
- 2019Sticking to the Facts: Confident Decoding for Faithful Data-to-Text Generationcited by 62
- 2021JointGT: Graph-Text Joint Representation Learning for Text Generation from Knowledge Graphscited by 58
- 2019Reinforcement Learning Based Graph-to-Sequence Model for Natural Question Generationcited by 57
- 2019Neural data-to-text generation: A comparison between pipeline and end-to-end architecturescited by 51
- 2021Structural Adapters in Pretrained Language Models for AMR-to-text Generationcited by 48
- 2020Evaluating Semantic Accuracy of Data-to-Text Generation with Natural Language Inferencecited by 43
- 2020GPT-too: A language-model-first approach for AMR-to-text generationcited by 38
- 2020CycleGT: Unsupervised Graph-to-Text and Text-to-Graph Generation via Cycle Trainingcited by 38
- 2020Have Your Text and Use It Too! End-to-End Neural Data-to-Text Generation with Semantic Fidelitycited by 35
- 2020Modeling Global and Local Node Contexts for Text Generation from Knowledge Graphscited by 30
- 2019Densely Connected Graph Convolutional Networks for Graph-to-Sequence Learningcited by 29
- 2020Toward Subgraph-Guided Knowledge Graph Question Generation with Graph Neural Networkscited by 25
- 2021Structural Information Preserving for Graph-to-Text Generationcited by 24
- 2020Promoting Graph Awareness in Linearized Graph-to-Text Generationcited by 14
- 2021Stage-wise Fine-tuning for Graph-to-Text Generationcited by 11
- 2017A Simple and Accurate Syntax-Agnostic Neural Model for Dependency-based Semantic Role Labelingcited by 10
- 2020Partially-Aligned Data-to-Text Generation with Distant Supervisioncited by 10
- 2022Conversational Agents: Theory and Applicationscited by 8
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