A Good Sample is Hard to Find: Noise Injection Sampling and
Self-Training for Neural Language Generation Models

A Good Sample is Hard to Find: Noise Injection Sampling and Self-Training for Neural Language Generation Models

8 November 2019

Kathleen McKeown

Papers citing "A Good Sample is Hard to Find: Noise Injection Sampling and Self-Training for Neural Language Generation Models"

8 / 8 papers shown

Title
Rethinking Semi-supervised Learning with Language Models Zhengxiang Shi Francesco Tonolini Nikolaos Aletras Emine Yilmaz G. Kazai Yunlong Jiao 32 18 0 22 May 2023
Innovations in Neural Data-to-text Generation: A Survey Mandar Sharma Ajay K. Gogineni Naren Ramakrishnan 29 10 0 25 Jul 2022
Jurassic is (almost) All You Need: Few-Shot Meaning-to-Text Generation for Open-Domain Dialogue Lena Reed Cecilia Li Angela Ramirez Liren Wu M. Walker 28 7 0 15 Oct 2021
Retrieve, Caption, Generate: Visual Grounding for Enhancing Commonsense in Text Generation Models Steven Y. Feng Kevin Lu Zhuofu Tao Malihe Alikhani Teruko Mitamura Eduard H. Hovy Varun Gangal LRM 30 13 0 08 Sep 2021
Improving Factual Consistency of Abstractive Summarization via Question Answering Feng Nan Cicero Nogueira dos Santos Henghui Zhu Patrick K. L. Ng Kathleen McKeown Ramesh Nallapati Dejiao Zhang Zhiguo Wang Andrew O. Arnold Bing Xiang HILM 6 82 0 10 May 2021
A Survey of Data Augmentation Approaches for NLP Steven Y. Feng Varun Gangal Jason W. Wei Sarath Chandar Soroush Vosoughi Teruko Mitamura Eduard H. Hovy AIMat 37 799 0 07 May 2021
Evaluating Semantic Accuracy of Data-to-Text Generation with Natural Language Inference Ondrej Dusek Zdeněk Kasner 22 64 0 21 Nov 2020
Convolutional Neural Networks for Sentence Classification Yoon Kim AILaw VLM 255 13,364 0 25 Aug 2014