Improving Embedding-based Large-scale Retrieval via Label Enhancement

no code implementations • Findings (EMNLP) 2021 • Peiyang Liu, Xi Wang, Sen Wang, Wei Ye, Xiangyu Xi, Shikun Zhang

Current embedding-based large-scale retrieval models are trained with 0-1 hard label that indicates whether a query is relevant to a document, ignoring rich information of the relevance degree.

Retrieval

Label Smoothing for Text Mining

no code implementations • COLING 2022 • Peiyang Liu, Xiangyu Xi, Wei Ye, Shikun Zhang

This paper presents a novel keyword-based LS method to automatically generate soft labels from hard labels via exploiting the relevance between labels and text instances.

Retrieval text-classification +2

QuadrupletBERT: An Efficient Model For Embedding-Based Large-Scale Retrieval

no code implementations • NAACL 2021 • Peiyang Liu, Sen Wang, Xi Wang, Wei Ye, Shikun Zhang

The embedding-based large-scale query-document retrieval problem is a hot topic in the information retrieval (IR) field.

Information Retrieval Retrieval