DESIRE-ME: Domain-Enhanced Supervised Information Retrieval Using Mixture-of-Experts

Author:

Kasela PranavORCID,Pasi GabriellaORCID,Perego RaffaeleORCID,Tonellotto NicolaORCID

Publisher

Springer Nature Switzerland

Reference30 articles.

1. LNCS;E Bassani,2022

2. Bassani, E.: Ranxhub: an online repository for information retrieval runs. In: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2023, pp. 3210–3214. Association for Computing Machinery, New York (2023). https://doi.org/10.1145/3539618.3591823

3. Collobert, R., Bengio, S., Bengio, Y.: A parallel mixture of SVMs for very large scale problems. In: Dietterich, T., Becker, S., Ghahramani, Z. (eds.) Advances in Neural Information Processing Systems, vol. 14. MIT Press (2001). https://proceedings.neurips.cc/paper_files/paper/2001/file/36ac8e558ac7690b6f44e2cb5ef93322-Paper.pdf

4. Dai, D., et al.: Mixture of experts for biomedical question answering. arXiv abs/2204.07469 (2022). https://api.semanticscholar.org/CorpusID:248218762

5. Dauphin, Y.N., Fan, A., Auli, M., Grangier, D.: Language modeling with gated convolutional networks. In: Precup, D., Teh, Y.W. (eds.) Proceedings of the 34th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 70, pp. 933–941. PMLR (2017). https://proceedings.mlr.press/v70/dauphin17a.html

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