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
- Editors:
- Sebastian Padó, Ruihong Huang
- 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
- 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-IJCNLP 2019)
- PDF:
- https://preview.aclanthology.org/naacl24-info/D19-3034.pdf