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20122023
most citedEnhancing Person-Job Fit for Talent Recruitment: An Ability-aware Neural Network Approach

155 citations · 928 across the 39 of their papers we have counts for

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Showing cs.CLShow all

12 papers · 1 filter

cs.CL2022

Graph Adaptive Semantic Transfer for Cross-domain Sentiment Classification

Kai Zhang, Qi Liu, Zhenya Huang +5

Cross-domain sentiment classification (CDSC) aims to use the transferable semantics learned from the source domain to predict the sentiment of reviews in the unlabeled target domai…

cs.CL2021

DGA-Net Dynamic Gaussian Attention Network for Sentence Semantic Matching

Kun Zhang, Guangyi Lv, Meng Wang +1

Sentence semantic matching requires an agent to determine the semantic relation between two sentences, where much recent progress has been made by the advancement of representation…

cs.CL20212 cited

Towards Variable-Length Textual Adversarial Attacks

Junliang Guo, Zhirui Zhang, Linlin Zhang +4

Adversarial attacks have shown the vulnerability of machine learning models, however, it is non-trivial to conduct textual adversarial attacks on natural language processing tasks…

cs.CL20211 cited

Inheritance-guided Hierarchical Assignment for Clinical Automatic Diagnosis

Yichao Du, Pengfei Luo, Xudong Hong +5

Clinical diagnosis, which aims to assign diagnosis codes for a patient based on the clinical note, plays an essential role in clinical decision-making. Considering that manual diag…

cs.CL2020

R-Net: Relation of Relation Learning Network for Sentence Semantic Matching

Kun Zhang, Le Wu, Guangyi Lv +3

Sentence semantic matching is one of the fundamental tasks in natural language processing, which requires an agent to determine the semantic relation among input sentences. Recentl…

cs.CL2020

Incorporating BERT into Parallel Sequence Decoding with Adapters

Junliang Guo, Zhirui Zhang, Linli Xu +3

While large scale pre-trained language models such as BERT have achieved great success on various natural language understanding tasks, how to efficiently and effectively incorpora…