%0 Conference Proceedings %T SEAGLE: A Platform for Comparative Evaluation of Semantic Encoders for Information Retrieval %A Schmidt, Fabian David %A Dietsche, Markus %A Ponzetto, Simone Paolo %A Glavaš, Goran %Y Padó, Sebastian %Y Huang, Ruihong %S 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 %D 2019 %8 November %I Association for Computational Linguistics %C Hong Kong, China %F schmidt-etal-2019-seagle %X We introduce Seagle, 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 cross-lingual sentence retrieval. Seagle functionality can be exploited via an easy-to-use web interface and its modular backend (micro-service architecture) can easily be extended with additional semantic search models. %R 10.18653/v1/D19-3034 %U https://aclanthology.org/D19-3034 %U https://doi.org/10.18653/v1/D19-3034 %P 199-204