554 citations · 1.6k across the 42 of their papers we have counts for
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HRKD: Hierarchical Relational Knowledge Distillation for Cross-domain Language Model Compression
Chenhe Dong, Yaliang Li, Ying Shen +1
On many natural language processing tasks, large pre-trained language models (PLMs) have shown overwhelming performances compared with traditional neural network methods. Neverthel…
Meta-Learning Adversarial Domain Adaptation Network for Few-Shot Text Classification
ChengCheng Han, Zeqiu Fan, Dongxiang Zhang +3
Meta-learning has emerged as a trending technique to tackle few-shot text classification and achieved state-of-the-art performance. However, existing solutions heavily rely on the…
Global Context Enhanced Graph Neural Networks for Session-based Recommendation
Ziyang Wang, Wei Wei, Gao Cong +3
Session-based recommendation (SBR) is a challenging task, which aims at recommending items based on anonymous behavior sequences. Almost all the existing solutions for SBR model us…
Kaleido-BERT: Vision-Language Pre-training on Fashion Domain
Mingchen Zhuge, Dehong Gao, Deng-Ping Fan +5
We present a new vision-language (VL) pre-training model dubbed Kaleido-BERT, which introduces a novel kaleido strategy for fashion cross-modality representations from transformers…
Learning to Augment for Data-Scarce Domain BERT Knowledge Distillation
Lingyun Feng, Minghui Qiu, Yaliang Li +2
Despite pre-trained language models such as BERT have achieved appealing performance in a wide range of natural language processing tasks, they are computationally expensive to be…