27 citations · 65 across the 7 of their papers we have counts for
12 papers
Debiasing Neural Retrieval via In-batch Balancing Regularization
Yuantong Li, Xiaokai Wei, Zijian Wang +4
People frequently interact with information retrieval (IR) systems, however, IR models exhibit biases and discrimination towards various demographics. The in-processing fair rankin…
DQ-BART: Efficient Sequence-to-Sequence Model via Joint Distillation and Quantization
Zheng Li, Zijian Wang, Ming Tan +5
Large-scale pre-trained sequence-to-sequence models like BART and T5 achieve state-of-the-art performance on many generative NLP tasks. However, such models pose a great challenge…
QaNER: Prompting Question Answering Models for Few-shot Named Entity Recognition
Andy T. Liu, Wei Xiao, Henghui Zhu +3
Recently, prompt-based learning for pre-trained language models has succeeded in few-shot Named Entity Recognition (NER) by exploiting prompts as task guidance to increase label ef…
Self-Supervised Speaker Verification with Simple Siamese Network and Self-Supervised Regularization
Mufan Sang, Haoqi Li, Fang Liu +2
Training speaker-discriminative and robust speaker verification systems without speaker labels is still challenging and worthwhile to explore. In this study, we propose an effectiv…
Knowledge Enhanced Pretrained Language Models: A Compreshensive Survey
Xiaokai Wei, Shen Wang, Dejiao Zhang +2
Pretrained Language Models (PLM) have established a new paradigm through learning informative contextualized representations on large-scale text corpus. This new paradigm has revol…
Contrastive Fine-tuning Improves Robustness for Neural Rankers
Xiaofei Ma, Cicero Nogueira dos Santos, Andrew O. Arnold
The performance of state-of-the-art neural rankers can deteriorate substantially when exposed to noisy inputs or applied to a new domain. In this paper, we present a novel method f…