38 citations · 93 across the 16 of their papers we have counts for
12 papers · 1 filter
Dial2vec: Self-Guided Contrastive Learning of Unsupervised Dialogue Embeddings
Che Liu, Rui Wang, Junfeng Jiang +2
In this paper, we introduce the task of learning unsupervised dialogue embeddings. Trivial approaches such as combining pre-trained word or sentence embeddings and encoding through…
Forging Multiple Training Objectives for Pre-trained Language Models via Meta-Learning
Hongqiu Wu, Ruixue Ding, Hai Zhao +4
Multiple pre-training objectives fill the vacancy of the understanding capability of single-objective language modeling, which serves the ultimate purpose of pre-trained language m…
MuCGEC: a Multi-Reference Multi-Source Evaluation Dataset for Chinese Grammatical Error Correction
Yue Zhang, Zhenghua Li, Zuyi Bao +5
This paper presents MuCGEC, a multi-reference multi-source evaluation dataset for Chinese Grammatical Error Correction (CGEC), consisting of 7,063 sentences collected from three Ch…
Probing Structured Pruning on Multilingual Pre-trained Models: Settings, Algorithms, and Efficiency
Yanyang Li, Fuli Luo, Runxin Xu +3
Structured pruning has been extensively studied on monolingual pre-trained language models and is yet to be fully evaluated on their multilingual counterparts. This work investigat…
Raise a Child in Large Language Model: Towards Effective and Generalizable Fine-tuning
Runxin Xu, Fuli Luo, Zhiyuan Zhang +4
Recent pretrained language models extend from millions to billions of parameters. Thus the need to fine-tune an extremely large pretrained model with a limited training corpus aris…
StructuralLM: Structural Pre-training for Form Understanding
Chenliang Li, Bin Bi, Ming Yan +4
Large pre-trained language models achieve state-of-the-art results when fine-tuned on downstream NLP tasks. However, they almost exclusively focus on text-only representation, whil…