Continually Adaptive Neural Retrieval Across the Legal, Patent and Health Domain

Author:

Althammer Sophia

Publisher

Springer International Publishing

Reference58 articles.

1. Akkalyoncu Yilmaz, Z., Wang, S., Yang, W., Zhang, H., Lin, J.: 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, Hong Kong, China, pp. 19–24. Association for Computational Linguistics, November 2019. https://doi.org/10.18653/v1/D19-3004. https://aclanthology.org/D19-3004

2. Akkalyoncu Yilmaz, Z., Yang, W., Zhang, H., Lin, J.: Cross-domain modeling of sentence-level evidence for document 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), Hong Kong, China, pp. 3490–3496. Association for Computational Linguistics, November 2019. https://doi.org/10.18653/v1/D19-1352. https://aclanthology.org/D19-1352

3. Althammer, S.: RUDI: real-time learning to update dense retrieval indices. In: Proceedings of DESIRES 2021–2nd International Conference on Design of Experimental Search Information REtrieval Systems (2021)

4. Althammer, S., Hofstätter, S., Hanbury, A.: Cross-domain retrieval in the legal and patent domains: a reproducibility study. In: Advances in Information Retrieval, 43rd European Conference on IR Research, ECIR 2021 (2021)

5. Askari, A., Verberne, S.: Combining lexical and neural retrieval with longformer-based summarization for effective case law retrieval. In: Proceedings of DESIRES 2021–2nd International Conference on Design of Experimental Search Information REtrieval Systems (2021)

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