257 citations · 469 across the 45 of their papers we have counts for
12 papers · 1 filter
Query Your Model with Definitions in FrameNet: An Effective Method for Frame Semantic Role Labeling
Ce Zheng, Yiming Wang, Baobao Chang
Frame Semantic Role Labeling (FSRL) identifies arguments and labels them with frame semantic roles defined in FrameNet. Previous researches tend to divide FSRL into argument identi…
A Two-Stage Method for Chinese AMR Parsing
Liang Chen, Bofei Gao, Baobao Chang
In this paper, we provide a detailed description of our system at CAMRP-2022 evaluation. We firstly propose a two-stage method to conduct Chinese AMR Parsing with alignment generat…
SCL-RAI: Span-based Contrastive Learning with Retrieval Augmented Inference for Unlabeled Entity Problem in NER
Shuzheng Si, Shuang Zeng, Jiaxing Lin +1
Named Entity Recognition is the task to locate and classify the entities in the text. However, Unlabeled Entity Problem in NER datasets seriously hinders the improvement of NER per…
A Double-Graph Based Framework for Frame Semantic Parsing
Ce Zheng, Xudong Chen, Runxin Xu +1
Frame semantic parsing is a fundamental NLP task, which consists of three subtasks: frame identification, argument identification and role classification. Most previous studies ten…
Robust Fine-tuning via Perturbation and Interpolation from In-batch Instances
Shoujie Tong, Qingxiu Dong, Damai Dai +4
Fine-tuning pretrained language models (PLMs) on downstream tasks has become common practice in natural language processing. However, most of the PLMs are vulnerable, e.g., they ar…
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…
A Roadmap for Big Model
Sha Yuan, Hanyu Zhao, Shuai Zhao +97
With the rapid development of deep learning, training Big Models (BMs) for multiple downstream tasks becomes a popular paradigm. Researchers have achieved various outcomes in the c…
StableMoE: Stable Routing Strategy for Mixture of Experts
Damai Dai, Li Dong, Shuming Ma +4
The Mixture-of-Experts (MoE) technique can scale up the model size of Transformers with an affordable computational overhead. We point out that existing learning-to-route MoE metho…
Mixture of Experts for Biomedical Question Answering
Damai Dai, Wenbin Jiang, Jiyuan Zhang +5
Biomedical Question Answering (BQA) has attracted increasing attention in recent years due to its promising application prospect. It is a challenging task because the biomedical qu…
DISK: Domain-constrained Instance Sketch for Math Word Problem Generation
Tianyang Cao, Shuang Zeng, Xiaodan Xu +2
A math word problem (MWP) is a coherent narrative which reflects the underlying logic of math equations. Successful MWP generation can automate the writing of mathematics questions…
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…