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

Fabian David Schmidt, Markus Dietsche, Simone Paolo Ponzetto, Goran Glavaš


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.
Anthology ID:
D19-3034
Volume:
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
Month:
November
Year:
2019
Address:
Hong Kong, China
Venues:
EMNLP | IJCNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
199–204
Language:
URL:
https://aclanthology.org/D19-3034
DOI:
10.18653/v1/D19-3034
Bibkey:
Cite (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.
Cite (Informal):
SEAGLE: A Platform for Comparative Evaluation of Semantic Encoders for Information Retrieval (Schmidt et al., EMNLP 2019)
Copy Citation:
PDF:
https://aclanthology.org/D19-3034.pdf