765 citations · 1.3k across the 25 of their papers we have counts for
11 papers · 1 filter
SimANS: Simple Ambiguous Negatives Sampling for Dense Text Retrieval
Kun Zhou, Yeyun Gong, Xiao Liu +8
Sampling proper negatives from a large document pool is vital to effectively train a dense retrieval model. However, existing negative sampling strategies suffer from the uninforma…
Great Truths are Always Simple: A Rather Simple Knowledge Encoder for Enhancing the Commonsense Reasoning Capacity of Pre-Trained Models
Jinhao Jiang, Kun Zhou, Wayne Xin Zhao +1
Commonsense reasoning in natural language is a desired ability of artificial intelligent systems. For solving complex commonsense reasoning tasks, a typical solution is to enhance…
Debiased Contrastive Learning of Unsupervised Sentence Representations
Kun Zhou, Beichen Zhang, Wayne Xin Zhao +1
Recently, contrastive learning has been shown to be effective in improving pre-trained language models (PLM) to derive high-quality sentence representations. It aims to pull close…
Virtual Data Augmentation: A Robust and General Framework for Fine-tuning Pre-trained Models
Kun Zhou, Wayne Xin Zhao, Sirui Wang +3
Recent works have shown that powerful pre-trained language models (PLM) can be fooled by small perturbations or intentional attacks. To solve this issue, various data augmentation…
BERT4SO: Neural Sentence Ordering by Fine-tuning BERT
Yutao Zhu, Jian-Yun Nie, Kun Zhou +3
Sentence ordering aims to arrange the sentences of a given text in the correct order. Recent work frames it as a ranking problem and applies deep neural networks to it. In this wor…
Neural Sentence Ordering Based on Constraint Graphs
Yutao Zhu, Kun Zhou, Jian-Yun Nie +2
Sentence ordering aims at arranging a list of sentences in the correct order. Based on the observation that sentence order at different distances may rely on different types of inf…