To Tune or Not To Tune? How About the Best of Both Worlds?

To Tune or Not To Tune? How About the Best of Both Worlds?

9 July 2019

ArXiv (abs)PDF HTML

Papers citing "To Tune or Not To Tune? How About the Best of Both Worlds?"

10 / 10 papers shown

Title
Cluster & Tune: Boost Cold Start Performance in Text Classification Eyal Shnarch Ariel Gera Alon Halfon Lena Dankin Leshem Choshen R. Aharonov Noam Slonim 69 22 0 20 Mar 2022
Natural Language Understanding for Argumentative Dialogue Systems in the Opinion Building Domain W. A. Abro Annalena Aicher Niklas Rach Stefan Ultes Wolfgang Minker Guilin Qi 78 34 0 03 Mar 2021
Self-Tuning for Data-Efficient Deep Learning Ximei Wang Jing Gao Mingsheng Long Jianmin Wang BDL 91 71 0 25 Feb 2021
Toward Cross-Lingual Definition Generation for Language Learners Cunliang Kong Liner Yang Tianzuo Zhang Qinan Fan Zhenghao Liu Yun-Nung Chen Erhong Yang AI4Ed 55 8 0 12 Oct 2020
Detecting and Classifying Malevolent Dialogue Responses: Taxonomy, Data and Methodology Yangjun Zhang Fajie Yuan Maarten de Rijke 60 11 0 21 Aug 2020
Improving accuracy and speeding up Document Image Classification through parallel systems Javier Ferrando J. L. Domínguez Jordi Torres Raul Garcia David García Daniel Garrido J. Cortada M. Valero 119 26 0 16 Jun 2020
The Cascade Transformer: an Application for Efficient Answer Sentence Selection Luca Soldaini Alessandro Moschitti 90 44 0 05 May 2020
Beyond Fine-tuning: Few-Sample Sentence Embedding Transfer Siddhant Garg Rohit Kumar Sharma Yingyu Liang 82 4 0 10 Apr 2020
TANDA: Transfer and Adapt Pre-Trained Transformer Models for Answer Sentence Selection Siddhant Garg Thuy Vu Alessandro Moschitti 106 216 0 11 Nov 2019
Leveraging Pre-trained Checkpoints for Sequence Generation Tasks S. Rothe Shashi Narayan Aliaksei Severyn SILM 155 438 0 29 Jul 2019