SEAGLE: A Platform for Comparative Evaluation of Semantic Encoders for Information Retrieval

Fabian David Schmidt, Markus Dietsche, Simone Paolo Ponzetto, Goran Glavaš
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  
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
more » ... . SEAGLE functionality can be exploited via an easy-to-use web interface and its modular backend (microservice architecture) can easily be extended with additional semantic search models.
doi:10.18653/v1/d19-3034 dblp:conf/emnlp/SchmidtDPG19 fatcat:kadnmiwinjduhmgn2ycmc5svwy