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    <abstract>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.</abstract>
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%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

Markdown (Informal)

[SEAGLE: A Platform for Comparative Evaluation of Semantic Encoders for Information Retrieval](https://aclanthology.org/D19-3034) (Schmidt et al., EMNLP-IJCNLP 2019)

• SEAGLE: A Platform for Comparative Evaluation of Semantic Encoders for Information Retrieval (Schmidt et al., EMNLP-IJCNLP 2019)

ACL

• Fabian David Schmidt, Markus Dietsche, Simone Paolo Ponzetto, and Goran Glavaš. 2019. SEAGLE: A Platform for Comparative Evaluation of Semantic Encoders for Information Retrieval. In 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, pages 199–204, Hong Kong, China. Association for Computational Linguistics.