Don't Take It Literally: An Edit-Invariant Sequence Loss for Text
Generation

Don't Take It Literally: An Edit-Invariant Sequence Loss for Text Generation

29 June 2021

Xiaodan Liang

Papers citing "Don't Take It Literally: An Edit-Invariant Sequence Loss for Text Generation"

7 / 7 papers shown

Title
Multi-Granularity Optimization for Non-Autoregressive Translation Yafu Li Leyang Cui Yongjing Yin Yue Zhang 19 6 0 20 Oct 2022
Composable Text Controls in Latent Space with ODEs Guangyi Liu Zeyu Feng Yuan Gao Zichao Yang Xiaodan Liang Junwei Bao Xiaodong He Shuguang Cui Zhen Li Zhiting Hu AI4CE DiffM 13 32 0 01 Aug 2022
Gradient-Based Constrained Sampling from Language Models Sachin Kumar Biswajit Paria Yulia Tsvetkov BDL 28 53 0 25 May 2022
COLD Decoding: Energy-based Constrained Text Generation with Langevin Dynamics Lianhui Qin Sean Welleck Daniel Khashabi Yejin Choi AI4CE 39 144 0 23 Feb 2022
Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation Yonghui Wu M. Schuster Z. Chen Quoc V. Le Mohammad Norouzi ... Alex Rudnick Oriol Vinyals G. Corrado Macduff Hughes J. Dean AIMat 716 6,724 0 26 Sep 2016
Deep Reinforcement Learning for Dialogue Generation Jiwei Li Will Monroe Alan Ritter Michel Galley Jianfeng Gao Dan Jurafsky 192 1,325 0 05 Jun 2016
Effective Approaches to Attention-based Neural Machine Translation Thang Luong Hieu H. Pham Christopher D. Manning 214 7,687 0 17 Aug 2015