Abstract
While BERT has been shown to be effective for passage retrieval, its maximum input length limitation poses a challenge when applying the model to document retrieval. In this work, we reproduce three passage score aggregation approaches proposed by Dai and Callan [5] for overcoming this limitation. After reproducing their results, we generalize their findings through experiments with a new dataset and experiment with other pretrained transformers that share similarities with BERT. We find that these BERT variants are not more effective for document retrieval in isolation, but can lead to increased effectiveness when combined with “pre–fine-tuning” on the MS MARCO passage dataset. Finally, we investigate whether there is a difference between fine-tuning models on “deep” judgments (i.e., fewer queries with many judgments each) vs. fine-tuning on “shallow” judgments (i.e., many queries with fewer judgments each). Based on available data from two different datasets, we find that the two approaches perform similarly.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
- 2.
- 3.
The length of BERT’s inputs cannot exceed 512 tokens. This includes the query, the passage, and the three special tokens. This limitation comes from the fact that position embeddings are used to encode BERT’s input; these position embeddings were only pretrained for sequences up to length 512.
- 4.
- 5.
- 6.
- 7.
- 8.
The hidden_dropout_prob configuration in HuggingFace’s library.
- 9.
- 10.
See line 58 of tools/bert_passage_result_to_trec.py in the original code.
References
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), pp. 3481–3487 (2019)
Allan, J., Carterette, B., Aslam, J.A., Pavlu, V., Dachev, B., Kanoulas, E.: Million query track 2007 overview. In: Proceedings of TREC 2007 (2007)
Bajaj, P., et al.: MS MARCO: a human generated machine reading comprehension dataset. arXiv preprint arXiv:1611.09268v3 (2018)
Clark, K., Luong, M.T., Le, Q.V., Manning, C.D.: ELECTRA: pre-training text encoders as discriminators rather than generators. arXiv preprint arXiv:2003.10555 (2020)
Dai, Z., Callan, J.: Deeper text understanding for IR with contextual neural language modeling. In: Proceedings of the 42nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2019), pp. 985–988 (2019)
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186 (2019)
Fan, Y., Guo, J., Lan, Y., Xu, J., Zhai, C., Cheng, X.: Modeling diverse relevance patterns in ad-hoc retrieval. In: Proceedings of the 41st International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 375–384 (2018)
Lan, Z., Chen, M., Goodman, S., Gimpel, K., Sharma, P., Soricut, R.: ALBERT: a lite BERT for self-supervised learning of language representations. arXiv preprint arXiv:1909.11942 (2019)
Li, C., Yates, A., MacAvaney, S., He, B., Sun, Y.: PARADE: passage representation aggregation for document reranking. arXiv preprint arXiv:2008.09093 (2020)
Lin, J., Nogueira, R., Yates, A.: Pretrained transformers for text ranking: BERT and beyond. arXiv preprint arXiv:2010.06467 (2020)
Liu, Y., et al.: RoBERTa: a robustly optimized BERT pretraining approach. arXiv preprint arXiv:1907.11692 (2019)
MacAvaney, S., Yates, A., Cohan, A., Goharian, N.: CEDR: contextualized embeddings for document ranking. In: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1101–1104 (2019)
Nogueira, R., Cho, K.: Passage re-ranking with BERT. arXiv preprint arXiv:1901.04085 (2019)
Pang, L., Lan, Y., Guo, J., Xu, J., Xu, J., Cheng, X.: DeepRank: a new deep architecture for relevance ranking in information retrieval. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pp. 257–266 (2017)
Wolf, T., et al.: HuggingFace’s transformers: state-of-the-art natural language processing. arXiv preprint arXiv:1910.03771 (2019)
Yang, P., Fang, H., Lin, J.: Anserini: enabling the use of Lucene for information retrieval research. In: Proceedings of the 40th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2017), pp. 1253–1256 (2017)
Yang, W., Lu, K., Yang, P., Lin, J.: Critically examining the “neural hype” weak baselines and the additivity of effectiveness gains from neural ranking models. In: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2019), pp. 1129–1132 (2019)
Yates, A., Jose, K.M., Zhang, X., Lin, J.: Flexible IR pipelines with Capreolus. In: Proceedings of the 29th ACM International Conference on Information and Knowledge Management, pp. 3181–3188 (2020)
Yilmaz, E., Robertson, S.E.: Deep versus shallow judgments in learning to rank. In: Proceedings of the 32nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2009), pp. 662–663 (2009)
Zhang, X., Yates, A., Lin, J.: A little bit is worse than none: Ranking with limited training data. In: Proceedings of SustaiNLP: Workshop on Simple and Efficient Natural Language Processing, pp. 107–112 (2020)
Acknowledgments
This research was supported in part by the Canada First Research Excellence Fund and the Natural Sciences and Engineering Research Council (NSERC) of Canada. In addition, we would like to thank Google Cloud and TensorFlow Research Cloud for credits to support this work.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Zhang, X., Yates, A., Lin, J. (2021). Comparing Score Aggregation Approaches for Document Retrieval with Pretrained Transformers. In: Hiemstra, D., Moens, MF., Mothe, J., Perego, R., Potthast, M., Sebastiani, F. (eds) Advances in Information Retrieval. ECIR 2021. Lecture Notes in Computer Science(), vol 12657. Springer, Cham. https://doi.org/10.1007/978-3-030-72240-1_11
Download citation
DOI: https://doi.org/10.1007/978-3-030-72240-1_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-72239-5
Online ISBN: 978-3-030-72240-1
eBook Packages: Computer ScienceComputer Science (R0)