A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
SEAGLE: A Platform for Comparative Evaluation of Semantic Encoders for Information Retrieval
2019
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations
Preserved Fulltext
We introduce SEAGLE, 1 a platform for comparative evaluation of semantic text encoding models on information retrieval (IR) tasks. SEAGLE implements (1) word embedding aggregators, which represent texts as algebraic aggregations of pretrained word embeddings and (2) pretrained semantic encoders, and allows for their comparative evaluation on arbitrary (monolingual and cross-lingual) IR collections. We benchmark SEAGLE's models on monolingual document retrieval and crosslingual sentence
doi:10.18653/v1/d19-3034
dblp:conf/emnlp/SchmidtDPG19
fatcat:kadnmiwinjduhmgn2ycmc5svwy