Control, Generate, Augment: A Scalable Framework for Multi-Attribute Text Generation

30 April 2020

Papers citing "Control, Generate, Augment: A Scalable Framework for Multi-Attribute Text Generation"

7 / 7 papers shown

Title
Boosting Reward Model with Preference-Conditional Multi-Aspect Synthetic Data Generation Jiaming Shen Ran Xu Yennie Jun Zhen Qin Tianqi Liu Carl Yang Yi Liang Simon Baumgartner Michael Bendersky SyDa 67 4 0 22 Jul 2024
Stranger Danger! Cross-Community Interactions with Fringe Users Increase the Growth of Fringe Communities on Reddit Giuseppe Russo Manoel Horta Ribeiro Robert West 24 10 0 18 Oct 2023
MACSum: Controllable Summarization with Mixed Attributes Yusen Zhang Yang Liu Ziyi Yang Yuwei Fang Yulong Chen Dragomir R. Radev Chenguang Zhu Michael Zeng Rui Zhang 34 15 0 09 Nov 2022
Disentangling Active and Passive Cosponsorship in the U.S. Congress Giuseppe Russo Christoph Gote L. Brandenberger Sophia Schlosser F. Schweitzer LLMSV AI4CE 24 7 0 19 May 2022
Controllable Dialogue Generation with Disentangled Multi-grained Style Specification and Attribute Consistency Reward Zhe Hu Zhiwei Cao Hou Pong Chan Jiachen Liu Xinyan Xiao Jinsong Su Hua Wu 35 9 0 14 Sep 2021
Learning from Multiple Noisy Augmented Data Sets for Better Cross-Lingual Spoken Language Understanding Yingmei Guo Linjun Shou J. Pei Ming Gong Mingxing Xu Zhiyong Wu Daxin Jiang 29 5 0 03 Sep 2021
Convolutional Neural Networks for Sentence Classification Yoon Kim AILaw VLM 255 13,364 0 25 Aug 2014