PIP: Parse-Instructed Prefix for Syntactically Controlled Paraphrase Generation

26 May 2023

Papers citing "PIP: Parse-Instructed Prefix for Syntactically Controlled Paraphrase Generation"

10 / 10 papers shown

Title
VTechAGP: An Academic-to-General-Audience Text Paraphrase Dataset and Benchmark Models Ming Cheng Jiaying Gong Chenhan Yuan William A. Ingram Edward A. Fox Hoda Eldardiry 42 0 0 07 Nov 2024
RST-LoRA: A Discourse-Aware Low-Rank Adaptation for Long Document Abstractive Summarization Dongqi Pu Vera Demberg 33 5 0 01 May 2024
Paraphrase Types for Generation and Detection Jan Philip Wahle Bela Gipp Terry Ruas 13 4 0 23 Oct 2023
Learning Easily Updated General Purpose Text Representations with Adaptable Task-Specific Prefixes Kuan-Hao Huang L Tan Rui Hou Sinong Wang Amjad Almahairi Ruty Rinott AI4CE 22 0 0 22 May 2023
SPoT: Better Frozen Model Adaptation through Soft Prompt Transfer Tu Vu Brian Lester Noah Constant Rami Al-Rfou Daniel Matthew Cer VLM LRM 137 277 0 15 Oct 2021
P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks Xiao Liu Kaixuan Ji Yicheng Fu Weng Lam Tam Zhengxiao Du Zhilin Yang Jie Tang VLM 236 804 0 14 Oct 2021
HypoGen: Hyperbole Generation with Commonsense and Counterfactual Knowledge Yufei Tian A. Sridhar Nanyun Peng 31 27 0 10 Sep 2021
The Power of Scale for Parameter-Efficient Prompt Tuning Brian Lester Rami Al-Rfou Noah Constant VPVLM 280 3,835 0 18 Apr 2021
Generating Syntactically Controlled Paraphrases without Using Annotated Parallel Pairs Kuan-Hao Huang Kai-Wei Chang 145 68 0 26 Jan 2021
Adversarial Example Generation with Syntactically Controlled Paraphrase Networks Mohit Iyyer John Wieting Kevin Gimpel Luke Zettlemoyer AAML GAN 185 711 0 17 Apr 2018