OpenMatch: An Open-Source Package for Information Retrieval.
Year: 2021
Type: preprint
Abstract: Information Retrieval (IR) is an important task and can be used in manyapplications. Neural IR (Neu-IR) models overcome the vocabulary mismatch
problem of sparse retrievers and thrive on the ranking pipeline with semantic
matching. Recent progress in IR mainly focuses on Neu-IR models, including
efficient dense retrieval, advanced neural architectures and robustly training
for few-shot IR that lacks training data. In order to integrate these
advantages for researchers and engineers to utilize and develop, OpenMatch
provides various functional neural modules based on PyTorch to maintain
sufficient extensibility, making it easy to build customized and
higher-capacity IR systems. Besides, OpenMatch consists of complicated
optimization tricks, various sparse/dense retrieval methods, and advanced
few-shot training methods, liberating users from surplus labor in baseline
reimplementation and neural model finetuning. With OpenMatch, we achieve
reasonable performance on various ranking datasets, rank first of the automatic
group in TREC COVID (Round 2) and rank top on the MS MARCO Document Ranking
leaderboard. The library, experimental methodologies and results of OpenMatch
are all publicly available at https://github.com/thunlp/OpenMatch.
Source: arXiv (Cornell University)
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3127152274