arXiv Open Access 2020

RepBERT: Contextualized Text Embeddings for First-Stage Retrieval

Jingtao Zhan Jiaxin Mao Yiqun Liu Min Zhang Shaoping Ma

Lihat Sumber

Abstrak

Although exact term match between queries and documents is the dominant method to perform first-stage retrieval, we propose a different approach, called RepBERT, to represent documents and queries with fixed-length contextualized embeddings. The inner products of query and document embeddings are regarded as relevance scores. On MS MARCO Passage Ranking task, RepBERT achieves state-of-the-art results among all initial retrieval techniques. And its efficiency is comparable to bag-of-words methods.

Topik & Kata Kunci

cs.IR

Penulis (5)

Jingtao Zhan

Jiaxin Mao

Yiqun Liu

Min Zhang

Shaoping Ma

Format Sitasi

APA MLA BibTeX

Zhan, J., Mao, J., Liu, Y., Zhang, M., Ma, S. (2020). RepBERT: Contextualized Text Embeddings for First-Stage Retrieval. https://arxiv.org/abs/2006.15498

Akses Cepat

Lihat di Sumber

Informasi Jurnal

Tahun Terbit: 2020
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓