Abstract
Adhoc retrieval is the task of effectively retrieving information for an end-user’s information need, usually expressed as a textual query. One of the most well-established retrieval frameworks is the two-stage retrieval pipeline, whereby an inexpensive retrieval algorithm retrieves a subset of candidate documents from a corpus, and a more sophisticated (but costly) model re-ranks these candidates. A notable limitation of this two-stage framework is that the second stage re-ranking model can only re-order documents, and any relevant documents not retrieved from the corpus in the first stage are entirely lost to the second stage. A recently-proposed Adaptive Re-Ranking technique has shown that extending the candidate pool by traversing a document similarity graph can overcome this recall problem. However, this traversal technique is agnostic of the user’s query, which has the potential to waste compute resources by scoring documents that are not related to the query. In this work, we propose an alternative formulation of the document similarity graph. Rather than using document similarities, we propose a weighted bipartite graph that consists of both document nodes and query nodes. This overcomes the limitations of prior Adaptive Re-Ranking approaches because the bipartite graph can be navigated in a manner that explicitly acknowledges the original user query issued to the search pipeline. We evaluate the effectiveness of our proposed framework by experimenting with the TREC Deep Learning track in a standard adhoc retrieval setting. We find that our approach outperforms state-of-the-art two-stage re-ranking pipelines, improving the nDCG@10 metric by 5.8% on the DL19 test collection.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Amati, G., Carpineto, C., Romano, G.: Query difficulty, robustness, and selective application of query expansion. In: Advances in Information Retrieval - 26th European Conference on Information Retrieval, pp. 127–137 (2004)
Amati, G., Van Rijsbergen, C.J.: Probabilistic models of information retrieval based on measuring the divergence from randomness. ACM Trans. Inf. Syst. (TOIS) 20(4), 357–389 (2002)
Boldi, P., Bonchi, F., Castillo, C., Donato, D., Gionis, A., Vigna, S.: The query-flow graph: model and applications. In: Proceedings of the 17th ACM Conference on Information and Knowledge Management, pp. 609–618 (2008)
Craswell, N., Mitra, B., Yilmaz, E., Campos, D., Voorhees, E.M.: Overview of the TREC 2019 deep learning track. In: Proceedings of the Twenty-Eighth Text REtrieval Conference (2019)
Craswell, N., Mitra, B., Yilmaz, E., Campos, D., Voorhees, E.M., Soboroff, I.: TREC deep learning track: Reusable test collections in the large data regime. In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 2369–2375 (2021)
Craswell, N., Szummer, M.: Random walks on the click graph. In: Proceedings of the 30th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 239–246 (2007)
Devlin, J., Chang, M., 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, NAACL-HLT, pp. 4171–4186 (2019)
Gospodinov, M., MacAvaney, S., Macdonald, C.: Doc2Query–: when less is more. In: Advances in Information Retrieval - 45th European Conference on Information Retrieval, pp. 414–422 (2023)
Hearst, M.A., Pedersen, J.O.: Reexamining the cluster hypothesis: scatter/gather on retrieval results. In: Proceedings of the 19th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 76–84 (1996)
Hofstätter, S., Hanbury, A.: Let’s measure run time! extending the IR replicability infrastructure to include performance aspects. In: Proceedings of the Open-Source IR Replicability Challenge co-located with 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 12–16 (2019)
Hofstätter, S., Lin, S., Yang, J., Lin, J., Hanbury, A.: Efficiently teaching an effective dense retriever with balanced topic aware sampling. In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 113–122 (2021)
Jaleel, N.A., et al.: UMass at TREC 2004: novelty and HARD. In: Proceedings of the Thirteenth Text REtrieval Conference (2004)
Jardine, N., van Rijsbergen, C.J.: The use of hierarchic clustering in information retrieval. Inf. Storage Retr. 7(5), 217–240 (1971)
Johnson, J., Douze, M., Jégou, H.: Billion-scale similarity search with GPUs. IEEE Trans. Big Data 7(3), 535–547 (2019)
Khattab, O., Zaharia, M.: ColBERT: efficient and effective passage search via contextualized late interaction over BERT. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 39–48 (2020)
Li, C., et al.: NPRF: a neural pseudo relevance feedback framework for ad-hoc information retrieval. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 4482–4491 (2018)
Li, H., Zhuang, S., Mourad, A., Ma, X., Lin, J., Zuccon, G.: Improving query representations for dense retrieval with pseudo relevance feedback: a reproducibility study. In: Advances in Information Retrieval - 44th European Conference on Information Retrieval, pp. 599–612 (2022)
Lin, J., Nogueira, R.F., Yates, A.: Pretrained Transformers for Text Ranking: BERT and Beyond. Morgan & Claypool Publishers, San Rafael (2021)
MacAvaney, S., Nardini, F.M., Perego, R., Tonellotto, N., Goharian, N., Frieder, O.: Efficient document re-ranking for transformers by precomputing term representations. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 49–58 (2020)
MacAvaney, S., Nardini, F.M., Perego, R., Tonellotto, N., Goharian, N., Frieder, O.: Expansion via prediction of importance with contextualization. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1573–1576 (2020)
MacAvaney, S., Tonellotto, N., Macdonald, C.: Adaptive re-ranking with a corpus graph. In: Proceedings of the 31st ACM International Conference on Information & Knowledge Management, pp. 1491–1500 (2022)
Macdonald, C., Tonellotto, N.: Declarative experimentation in information retrieval using PyTerrier. In: Proceedings of the 2020 ACM SIGIR International Conference on the Theory of Information Retrieval, pp. 161–168 (2020)
Nguyen, T., MacAvaney, S., Yates, A.: A unified framework for learned sparse retrieval. In: Advances in Information Retrieval - 45th European Conference on Information Retrieval, pp. 101–116 (2023)
Nogueira, R., Lin, J.: From doc2query to docTTTTTquery (2019). https://cs.uwaterloo.ca/~jimmylin/publications/Nogueira_Lin_2019_docTTTTTquery-v2.pdf
Nogueira, R.F., Yang, W., Lin, J., Cho, K.: Document expansion by query prediction. CoRR abs/1904.08375 (2019)
Pickens, J., Cooper, M., Golovchinsky, G.: Reverted indexing for feedback and expansion. In: Proceedings of the 19th ACM Conference on Information and Knowledge Management, pp. 1049–1058 (2010)
Pradeep, R., Liu, Y., Zhang, X., Li, Y., Yates, A., Lin, J.: Squeezing water from a stone: a bag of tricks for further improving cross-encoder effectiveness for reranking. In: Advances in Information Retrieval - 44th European Conference on Information Retrieval, pp. 655–670 (2022)
Raffel, C.: Exploring the limits of transfer learning with a unified text-to-text transformer. J. Mach. Learn. Res. 21(1), 5485–5551 (2020)
Robertson, S.E., Walker, S., Jones, S., Hancock-Beaulieu, M., Gatford, M.: Okapi at TREC-3. In: Proceedings of the Third Text REtrieval Conference, pp. 109–126 (1994)
Robertson, S.E., Zaragoza, H.: The probabilistic relevance framework: BM25 and beyond. Found. Trends Inf. Retr. 3(4), 333–389 (2009)
Rocchio Jr, J.J.: Relevance feedback in information retrieval. The SMART retrieval system: experiments in automatic document processing (1971)
Salamat, S., Arabzadeh, N., Zarrinkalam, F., Zihayat, M., Bagheri, E.: Learning query-space document representations for high-recall retrieval. In: Advances in Information Retrieval - 45th European Conference on Information Retrieval, pp. 599–607 (2023)
Scells, H., Zhuang, S., Zuccon, G.: Reduce, reuse, recycle: green information retrieval research. In: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 2825–2837 (2022)
Voorhees, E.M.: The cluster hypothesis revisited. In: Proceedings of the 8th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 188–196 (1985)
Wang, X., Macdonald, C., Tonellotto, N., Ounis, I.: Pseudo-relevance feedback for multiple representation dense retrieval. In: Proceedings of the 2021 ACM SIGIR International Conference on the Theory of Information Retrieval, pp. 297–306 (2021)
Xiong, L., et al.: Approximate nearest neighbor negative contrastive learning for dense text retrieval. In: 9th International Conference on Learning Representations (2021)
Yang, Z., Dai, Z., Yang, Y., Carbonell, J.G., Salakhutdinov, R., Le, Q.V.: XLNet: generalized autoregressive pretraining for language understanding. In: Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, pp. 5754–5764 (2019)
Yu, H., Xiong, C., Callan, J.: Improving query representations for dense retrieval with pseudo relevance feedback. In: Proceedings of the 30th ACM International Conference on Information & Knowledge Management, pp. 3592–3596 (2021)
Acknowledgement
We acknowledge EPSRC grant EP/R018634/1: Closed-Loop Data Science for Complex, Computationally- & Data-Intensive Analytics. We thank the anonymous reviewers for their helpful feedback on this manuscript.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Frayling, E., MacAvaney, S., Macdonald, C., Ounis, I. (2024). Effective Adhoc Retrieval Through Traversal of a Query-Document Graph. In: Goharian, N., et al. Advances in Information Retrieval. ECIR 2024. Lecture Notes in Computer Science, vol 14610. Springer, Cham. https://doi.org/10.1007/978-3-031-56063-7_6
Download citation
DOI: https://doi.org/10.1007/978-3-031-56063-7_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-56062-0
Online ISBN: 978-3-031-56063-7
eBook Packages: Computer ScienceComputer Science (R0)