Papers with graph-to-text generation
Stage-wise Fine-tuning for Graph-to-Text Generation (2021.acl-srw)
Copied to clipboard
| Challenge: | Graph-to-text generation has benefited from pre-trained language models (PLMs) but they fail to fully utilize the structure information of the input graph. |
| Approach: | They propose a structured graph-to-text model with a two-step fine-tuning mechanism which first fine-tracks model on Wikipedia before adapting to graph- to-text generation. |
| Outcome: | The proposed model improves the performance of the English WebNLG 2017 dataset by using tree-level embeddings to capture the inter-dependency structures of the input graph. |
Building Hierarchically Disentangled Language Models for Text Generation with Named Entities (2020.coling-main)
Copied to clipboard
| Challenge: | Named entities pose a unique challenge to traditional methods of language modeling. |
| Approach: | They propose a Hierarchically Disentangled Model for named entities in cooking recipes using a dataset from several publicly available online sources. |
| Outcome: | The proposed model is based on 158,473 cooking recipes from public sources. |
Text Generation from Knowledge Graphs with Graph Transformers (N19-1)
Copied to clipboard
| Challenge: | Existing methods for generating text with structured inputs are expensive and require manual annotation. |
| Approach: | They propose a graph transforming encoder which leverages relational structure of knowledge graphs without imposing linearization or hierarchical constraints. |
| Outcome: | The proposed system produces more informative texts than competing methods. |
Structural Adapters in Pretrained Language Models for AMR-to-Text Generation (2021.emnlp-main)
Copied to clipboard
| Challenge: | Pretrained language models (PLMs) have advanced graph-to-text generation, but efficient encoding of graph structure is challenging because of the nature of the data. |
| Approach: | They propose a method to encode graph structure into pretrained language models by training only graph structure-aware adapter parameters. |
| Outcome: | The proposed method outperforms the state-of-the-art on two AMR-to-text datasets, training only 5.1% of the adapter parameters. |
Evaluating and Improving Graph to Text Generation with Large Language Models (2025.naacl-long)
Copied to clipboard
| Challenge: | Recent advances in large language models have revolutionized natural language processing due to their zero-and-short-shot capabilities. |
| Approach: | They propose a tuning-free prompting approach for graph-to-text generation tasks. |
| Outcome: | The proposed approach improves LLMs on graph-to-text generation tasks incrementally. |
An Unsupervised Joint System for Text Generation from Knowledge Graphs and Semantic Parsing (2020.emnlp-main)
Copied to clipboard
| Challenge: | Knowledge graphs (KGs) vary greatly from one domain to another, resulting in a lack of domain-specific parallel graph-text data. |
| Approach: | They propose an unsupervised approach to graph-to-text generation and text-to graph knowledge extraction using WebNLG v2.1 and a new benchmark leveraging scene graphs from Visual Genome. |
| Outcome: | The proposed approach outperforms baselines on WebNLG v2.1 and a new benchmark leveraging scene graphs from Visual Genome. |
Structural Information Preserving for Graph-to-Text Generation (2020.acl-main)
Copied to clipboard
| Challenge: | Existing models that mess up or drop the core structural information of input graphs are lacking in graph-to-text generation. |
| Approach: | They propose to leverage richer training signals to guide a graph-to-text generation model by focusing on autoencoding losses and back-propagating the losses to better calibrate the model. |
| Outcome: | Experiments on two benchmarks show the proposed model over a state-of-the-art model . two types of autoencoding losses are used to back-propagate the model based on multitask training . |