Eval all, trust a few, do wrong to none: Comparing sentence generation models

21 April 2018

Papers citing "Eval all, trust a few, do wrong to none: Comparing sentence generation models"

9 / 9 papers shown

Title
Measuring Diversity in Synthetic Datasets Yuchang Zhu Huizhe Zhang Bingzhe Wu Jintang Li Zibin Zheng Peilin Zhao Liang Chen Yatao Bian 100 0 0 12 Feb 2025
MAD Speech: Measures of Acoustic Diversity of Speech Matthieu Futeral A. Agostinelli Marco Tagliasacchi Neil Zeghidour Eugene Kharitonov 54 1 0 16 Apr 2024
The Vendi Score: A Diversity Evaluation Metric for Machine Learning Dan Friedman Adji Bousso Dieng EGVM 94 111 0 05 Oct 2022
How Faithful is your Synthetic Data? Sample-level Metrics for Evaluating and Auditing Generative Models Ahmed Alaa B. V. Breugel Evgeny S. Saveliev M. Schaar 59 186 0 17 Feb 2021
Plug and Play Autoencoders for Conditional Text Generation Florian Mai Nikolaos Pappas Ivan Montero Noah A. Smith U. Washington 25 36 0 06 Oct 2020
Educating Text Autoencoders: Latent Representation Guidance via Denoising T. Shen Jonas W. Mueller Regina Barzilay Tommi Jaakkola 19 4 0 29 May 2019
Judge the Judges: A Large-Scale Evaluation Study of Neural Language Models for Online Review Generation Cristina Garbacea Samuel Carton Shiyan Yan Qiaozhu Mei ELM 25 29 0 02 Jan 2019
On Accurate Evaluation of GANs for Language Generation Stanislau Semeniuta Aliaksei Severyn Sylvain Gelly EGVM 39 81 0 13 Jun 2018
Assessing Generative Models via Precision and Recall Mehdi S. M. Sajjadi Olivier Bachem Mario Lucic Olivier Bousquet Sylvain Gelly EGVM 34 565 0 31 May 2018