Applying BERT to Document Retrieval with Birch
Zeynep Akkalyoncu Yilmaz, Shengjin Wang, Wei Yang, Haotian Zhang, Jimmy Lin
Abstract
We present Birch, a system that applies BERT to document retrieval via integration with the open-source Anserini information retrieval toolkit to demonstrate end-to-end search over large document collections. Birch implements simple ranking models that achieve state-of-the-art effectiveness on standard TREC newswire and social media test collections. This demonstration focuses on technical challenges in the integration of NLP and IR capabilities, along with the design rationale behind our approach to tightly-coupled integration between Python (to support neural networks) and the Java Virtual Machine (to support document retrieval using the open-source Lucene search library). We demonstrate integration of Birch with an existing search interface as well as interactive notebooks that highlight its capabilities in an easy-to-understand manner.- Anthology ID:
- D19-3004
- 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:
- 19–24
- Language:
- URL:
- https://aclanthology.org/D19-3004
- DOI:
- 10.18653/v1/D19-3004
- Cite (ACL):
- Zeynep Akkalyoncu Yilmaz, Shengjin Wang, Wei Yang, Haotian Zhang, and Jimmy Lin. 2019. Applying BERT to Document Retrieval with Birch. 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 19–24, Hong Kong, China. Association for Computational Linguistics.
- Cite (Informal):
- Applying BERT to Document Retrieval with Birch (Akkalyoncu Yilmaz et al., EMNLP-IJCNLP 2019)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/D19-3004.pdf