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Despite the effectiveness of utilizing the BERT model for document ranking, the high computational cost of such approaches limits their uses.
2015
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Micikevicius, P., Narang, S., Alben, J., Diamos, G.F., Elsen, E., García, D., Ginsburg, B., Houston, M., Kuchaiev, O., Venkatesh, G., Wu, H.: Mixed precision training. In: ICLR (Poster). OpenReview.net (2018)
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Dai, Z., Callan, J.: Deeper text understanding for IR with contextual neural language modeling. In: SIGIR. pp. 985–988. ACM (2019). https://doi.org/10.1145/3331184.3331303
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Devlin, J., Chang, M., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: NAACL-HLT (1). pp. 4171–4186. Association for Computational Linguistics (2019). https://doi.org/10.18653/v1/n19-1423
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Hofstätter, S., Hanbury, A.: Let’s measure run time! extending the IR replicability infrastructure to include performance aspects. In: OSIRRC@SIGIR. CEUR Workshop Proceedings, vol. 2409, pp. 12–16. CEUR-WS.org (2019)
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MacAvaney, S., Yates, A., Cohan, A., Goharian, N.: CEDR: contextualized embeddings for document ranking. In: SIGIR. pp. 1101–1104. ACM (2019). https://doi.org/10.1145/3331184.3331317
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Nogueira, R., Cho, K.: Passage re-ranking with BERT. CoRR abs/1901.04085
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Sun, S., Cheng, Y., Gan, Z., Liu, J.: Patient knowledge distillation for BERT model compression. In: EMNLP/IJCNLP (1). pp. 4322–4331. Association for Computational Linguistics (2019). https://doi.org/10.18653/v1/D19-1441
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2020
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Gao, L., Dai, Z., Callan, J.: Understanding BERT rankers under distillation. In: ICTIR. pp. 149–152. ACM (2020)
2020
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Jiao, X., Yin, Y., Shang, L., Jiang, X., Chen, X., Li, L., Wang, F., Liu, Q.: Tinybert: Distilling BERT for natural language understanding. In: EMNLP (Findings). pp. 4163–4174. Association for Computational Linguistics (2020). https://doi.org/10.18653/v1/2020.findings-emnlp.372
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Nogueira, R., Jiang, Z., Pradeep, R., Lin, J.: Document ranking with a pretrained sequence-to-sequence model. In: EMNLP (Findings). pp. 708–718. Association for Computational Linguistics (2020). https://doi.org/10.18653/v1/2020.findings-emnlp.63
2020
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2019
Cited alongside, same era.
2019
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Yilmaz, Z.A., Wang, S., Yang, W., Zhang, H., Lin, J.: Applying BERT to document retrieval with birch. In: EMNLP/IJCNLP (3). pp. 19–24. Association for Computational Linguistics (2019). https://doi.org/10.18653/v1/D19-3004
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Sun, Z., Yu, H., Song, X., Liu, R., Yang, Y., Zhou, D.: Mobilebert: a compact task-agnostic BERT for resource-limited devices. In: ACL. pp. 2158–2170. Association for Computational Linguistics (2020). https://doi.org/10.18653/v1/2020.acl-main.195
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Wang, W., Wei, F., Dong, L., Bao, H., Yang, N., Zhou, M.: Minilm: Deep self-attention distillation for task-agnostic compression of pre-trained transformers. In: NeurIPS (2020)
2020
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