12 citations · 30 across the 13 of their papers we have counts for
13 papers
A Two-Stream AMR-enhanced Model for Document-level Event Argument Extraction
Runxin Xu, Peiyi Wang, Tianyu Liu +3
Most previous studies aim at extracting events from a single sentence, while document-level event extraction still remains under-explored. In this paper, we focus on extracting eve…
ATP: AMRize Then Parse! Enhancing AMR Parsing with PseudoAMRs
Liang Chen, Peiyi Wang, Runxin Xu +3
As Abstract Meaning Representation (AMR) implicitly involves compound semantic annotations, we hypothesize auxiliary tasks which are semantically or formally related can better enh…
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…
Making Pre-trained Language Models End-to-end Few-shot Learners with Contrastive Prompt Tuning
Ziyun Xu, Chengyu Wang, Minghui Qiu +4
Pre-trained Language Models (PLMs) have achieved remarkable performance for various language understanding tasks in IR systems, which require the fine-tuning process based on label…
Focus on the Target's Vocabulary: Masked Label Smoothing for Machine Translation
Liang Chen, Runxin Xu, Baobao Chang
Label smoothing and vocabulary sharing are two widely used techniques in neural machine translation models. However, we argue that simply applying both techniques can be conflictin…
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…