Improving Compositional Generalization with Self-Training for Data-to-Text Generation

16 October 2021

Papers citing "Improving Compositional Generalization with Self-Training for Data-to-Text Generation"

7 / 7 papers shown

Title
Deciphering the Role of Representation Disentanglement: Investigating Compositional Generalization in CLIP Models Reza Abbasi M. Rohban M. Baghshah CoGe 38 5 0 08 Jul 2024
Joint Dropout: Improving Generalizability in Low-Resource Neural Machine Translation through Phrase Pair Variables Ali Araabi Vlad Niculae Christof Monz 29 1 0 24 Jul 2023
DSI++: Updating Transformer Memory with New Documents Sanket Vaibhav Mehta Jai Gupta Yi Tay Mostafa Dehghani Vinh Q. Tran J. Rao Marc Najork Emma Strubell Donald Metzler CLL 30 39 0 19 Dec 2022
Neural Pipeline for Zero-Shot Data-to-Text Generation Zdeněk Kasner Ondrej Dusek 9 33 0 30 Mar 2022
Scale Efficiently: Insights from Pre-training and Fine-tuning Transformers Yi Tay Mostafa Dehghani J. Rao W. Fedus Samira Abnar Hyung Won Chung Sharan Narang Dani Yogatama Ashish Vaswani Donald Metzler 188 110 0 22 Sep 2021
Scaling Laws for Neural Language Models Jared Kaplan Sam McCandlish T. Henighan Tom B. Brown B. Chess R. Child Scott Gray Alec Radford Jeff Wu Dario Amodei 226 4,453 0 23 Jan 2020
Revisiting Self-Training for Neural Sequence Generation Junxian He Jiatao Gu Jiajun Shen MarcÁurelio Ranzato SSL LRM 242 269 0 30 Sep 2019