activity
20172023
most citedKnowPrompt: Knowledge-aware Prompt-tuning with Synergistic Optimization for Relation Extraction

370 citations · 1.4k across the 60 of their papers we have counts for

collaborators

60 papers

cs.CL2023★ 5 cited

Continual Multimodal Knowledge Graph Construction

Xiang Chen, Jintian Zhang, Xiaohan Wang +5

Current Multimodal Knowledge Graph Construction (MKGC) models struggle with the real-world dynamism of continuously emerging entities and relations, often succumbing to catastrophi…

cs.CL2022★ 4 cited

From Cloze to Comprehension: Retrofitting Pre-trained Masked Language Model to Pre-trained Machine Reader

Weiwen Xu, Xin Li, Wenxuan Zhang +4

We present Pre-trained Machine Reader (PMR), a novel method for retrofitting pre-trained masked language models (MLMs) to pre-trained machine reading comprehension (MRC) models wit…

cs.CL2022

Competency-Aware Neural Machine Translation: Can Machine Translation Know its Own Translation Quality?

Pei Zhang, Baosong Yang, Haoran Wei +4

Neural machine translation (NMT) is often criticized for failures that happen without awareness. The lack of competency awareness makes NMT untrustworthy. This is in sharp contrast…

cs.CL2022

A Dataset for Hyper-Relational Extraction and a Cube-Filling Approach

Yew Ken Chia, Lidong Bing, Sharifah Mahani Aljunied +2

Relation extraction has the potential for large-scale knowledge graph construction, but current methods do not consider the qualifier attributes for each relation triplet, such as…

cs.CL2022★ 2 cited

ConNER: Consistency Training for Cross-lingual Named Entity Recognition

Ran Zhou, Xin Li, Lidong Bing +3

Cross-lingual named entity recognition (NER) suffers from data scarcity in the target languages, especially under zero-shot settings. Existing translate-train or knowledge distilla…

cs.CL2022★ 1 cited

Towards Robust Low-Resource Fine-Tuning with Multi-View Compressed Representations

Linlin Liu, Xingxuan Li, Megh Thakkar +4

Due to the huge amount of parameters, fine-tuning of pretrained language models (PLMs) is prone to overfitting in the low resource scenarios. In this work, we present a novel metho…