Improving Text-to-Text Pre-trained Models for the Graph-to-Text Task

By Zixiaofan Yang, Arash Einolghozati, Hakan Inan, K. Diedrick, Angela Fan, Pinar E. Donmez, Sonal Gupta. 2020

Subfields: Natural Language Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Natural Language Processing

1. Soumya Batra
2. Ankit Arun
3. Shashank Jain
4. Peyman Heidari
5. Michael. White
6. Payel Das
7. Shawn Mei
8. Pinar E. Donmez

Citations in the corpus

• Structure-to-Text Generation with Self-Training, Acceptability Classifiers and Context-Conditioning for the GEM Shared Task (2021, methodology): “For the WebNLG dataset, we followed Yang et al. (2020) in ordering the input triples using their implicit tree structure.”
• Getting to Production with Few-shot Natural Language Generation Models (2021, background): “Pre-trained language models have shown promising results for generation tasks such as translation, summarization and data-to-text (Lewis et al., 2020; Yang et al., 2020).”
• ReGen: Reinforcement Learning for Text and Knowledge Base Generation using Pretrained Language Models (2021, methodology): “…BLEU ↑ BLEU ↑ NLTK METEOR ↑ chrF++ ↑ Team/model Amazon AI (Shanghai) (Guo et al., 2020a) 0.540 0.535 0.417 0.690 OSU Neural NLG (Li et al., 2020) 0.535 0.532 0.414 0.688 FBConvAI (Yang et al., 2020) 0.527 0.523 0.413 0.686 bt5 (Agarwal et al., 2020) 0 did not lead to any noticeable improvements.”