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20202023
most citedGenerating similes effortlessly like a Pro: A Style Transfer Approach for Simile Generation

4 citations · 7 across the 7 of their papers we have counts for

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cs.CL2023

Open-Domain Text Evaluation via Contrastive Distribution Methods

Sidi Lu, Hongyi Liu, Asli Celikyilmaz +2

Recent advancements in open-domain text generation, driven by the power of large pre-trained language models (LLMs), have demonstrated remarkable performance. However, assessing th…

cs.CL2023

Unsupervised Melody-to-Lyric Generation

Yufei Tian, Anjali Narayan-Chen, Shereen Oraby +8

Automatic melody-to-lyric generation is a task in which song lyrics are generated to go with a given melody. It is of significant practical interest and more challenging than uncon…

cs.CL2022★ 1 cited

DOC: Improving Long Story Coherence With Detailed Outline Control

Kevin Yang, Dan Klein, Nanyun Peng +1

We propose the Detailed Outline Control (DOC) framework for improving long-range plot coherence when automatically generating several-thousand-word-long stories. DOC consists of tw…

cs.CL2022

NewsEdits: A News Article Revision Dataset and a Document-Level Reasoning Challenge

Alexander Spangher, Xiang Ren, Jonathan May +1

News article revision histories provide clues to narrative and factual evolution in news articles. To facilitate analysis of this evolution, we present the first publicly available…

cs.CL2022

Zero-shot Commonsense Question Answering with Cloze Translation and Consistency Optimization

Zi-Yi Dou, Nanyun Peng

Commonsense question answering (CQA) aims to test if models can answer questions regarding commonsense knowledge that everyone knows. Prior works that incorporate external knowledg…

cs.CL2020★ 4 cited

Generating similes effortlessly like a Pro: A Style Transfer Approach for Simile Generation

Tuhin Chakrabarty, Smaranda Muresan, Nanyun Peng

Literary tropes, from poetry to stories, are at the crux of human imagination and communication. Figurative language such as a simile go beyond plain expressions to give readers ne…