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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 sentencedoi:10.18653/v1/d19-3034 dblp:conf/emnlp/SchmidtDPG19 fatcat:kadnmiwinjduhmgn2ycmc5svwy