Understanding the role of human-inspired heuristics for retrieval models
Xiangsheng LI, Yiqun LIU, Jiaxin MAO
Understanding the role of human-inspired heuristics for retrieval models
Relevance estimation is one of the core concerns of information retrieval (IR) studies. Although existing retrieval models gained much success in both deepening our understanding of information seeking behavior and building effective retrieval systems, we have to admit that the models work in a rather different manner from how humans make relevance judgments. Users’ information seeking behaviors involve complex cognitive processes, however, the majority of these behavior patterns are not considered in existing retrieval models. To bridge the gap between practical user behavior and retrieval model, it is essential to systematically investigate user cognitive behavior during relevance judgement and incorporate these heuristics into retrieval models. In this paper, we aim to formally define a set of basic user reading heuristics during relevance judgement and investigate their corresponding modeling strategies in retrieval models. Further experiments are conducted to evaluate the effectiveness of different reading heuristics for improving ranking performance. Based on a large-scale Web search dataset, we find that most reading heuristics can improve the performance of retrieval model and establish guidelines for improving the design of retrieval models with humaninspired heuristics. Our study sheds light on building retrieval model from the perspective of cognitive behavior.
reading heuristics / retrieval model / cognitive be-havior
[1] |
Li X S, Mao J X, Wang C, Liu Y Q, Zhang M, Ma S P. Understanding reading attention distribution during relevance judgement. In: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval. 2019, 795–804
|
[2] |
Pang L, Lan Y Y, Guo J F, Xu J, Cheng X Q. Deeprank: a new deep architecture for relevance ranking in information retrieval. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. 2017
CrossRef
Google scholar
|
[3] |
Hu B T, Lu Z D, Li H, Chen Q C. Convolutional neural network architectures for matching natural language sentences. In: Proceedings of the 27th International Conference on Neural Information Processing Systems. 2014, 2042–2050
|
[4] |
Huang P S, He X D, Gao J F, Deng L, Acero A, Heck L. Learning deep structured semantic models for Web search using clickthrough data. In: Proceedings of the 22nd ACM International Conference on Information and Knowledge Management. 2013, 2333–2338
CrossRef
Google scholar
|
[5] |
Li X S, Liu Y Q, Mao J X, He Z X, Zhang M, Ma S P. Understanding reading attention distribution during relevance judgement. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management. 2018, 733–742
CrossRef
Google scholar
|
[6] |
Fan Y X, Guo J F, Lan Y Y, Xu J, Zhai C X, Cheng X Q.Modeling diverse relevance patterns in ad-hoc retrieval. In: Proceedings of the 41st ACM International Conference on Information and Knowledge Management. 2018, 375–384
CrossRef
Google scholar
|
[7] |
Robertson S E, Walker S. Some simple effective approximations to the 2-poisson model for probabilistic weighted retrieval. In: Proceedings of the 14th ACM International Conference on Information and Knowledge Management. 1994, 232–241
CrossRef
Google scholar
|
[8] |
Guo J F, Fan Y X, Ai Q Y, Croft W B. A deep relevance matching model for ad-hoc retrieval. In: Proceedings of the 25th ACM International Conference on Information and Knowledge Management. 2016, 55–64
CrossRef
Google scholar
|
[9] |
Xiong C, Dai Z, Callan J, Liu Z, Power R. End-to-end neural ad-hoc ranking with kernel pooling. In: Proceedings of the 40th ACM International Conference on Information and Knowledge Management. 2017
CrossRef
Google scholar
|
[10] |
Ding M, Zhou C, Chen Q B, Yang H X, Tang J. Cognitive graph for multi-hop reading comprehension at scale. 2019, arXiv preprint arXiv:1905.05460
CrossRef
Google scholar
|
[11] |
Yu A W, Lee H, Le Q V. Learning to skim text. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics. 2017, 1880–1890
CrossRef
Google scholar
|
[12] |
Fu T J, Ma W Y. Speed reading: learning to read forbackward via shuttle. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. 2018, 4439–4448
CrossRef
Google scholar
|
[13] |
Pang L, Lan Y Y, Guo J F, Xu J, Cheng X Q. A deep investigation of deep ir models. 2017, arXiv preprint arXiv:1707.07700
|
[14] |
Hui K, Yates A, Berberich K, de Melo G. Pacrr: a position-aware neural ir model for relevance matching. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. 2017, 1049–1058
CrossRef
Google scholar
|
[15] |
Fang H, Tao T, Zhai C X. Diagnostic evaluation of information retrieval models. Transactions on Information Systems, 2011, 29(2): 7
CrossRef
Google scholar
|
[16] |
Fang H, Tao T, Zhai C X. A formal study of information retrieval heuristics. In: Proceedings of the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. 2004, 49–56
CrossRef
Google scholar
|
[17] |
Tao T, Zhai C X. An exploration of proximity measures in information retrieval. In: Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. 2007, 295–302
CrossRef
Google scholar
|
[18] |
Hahn M, Keller F. Modeling human reading with neural attention. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. 2016, 85–95
CrossRef
Google scholar
|
[19] |
Liu X G, Mou L L, Cui H T, Lu Z D, Song S. Jumper: learning when to make classification decisions in reading. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence. 2018, 4237–4243
CrossRef
Google scholar
|
[20] |
Yu K Y, Liu Y, Schwing A G, Peng J. Fast accurate text classification: skimming, rereading and early stopping. In: Proceedings of the 6th International Conference on Learning Representations. 2018
|
[21] |
Wason P C, Evans J S B. Dual Processes in Reasoning? Cognition. Elsevier, 1974, 141–154
CrossRef
Google scholar
|
[22] |
Gehring J, Auli M, Grangier D, Yarats D, Dauphin Y N. Convolutional sequence to sequence learning. In: Proceedings of the 34th International Conference on Machine Learning. 2017, 1243–1252
|
[23] |
Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez A N, Polosukhin I. Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. 2017, 5998–6008
|
[24] |
Pang L, Lan Y Y, Guo J F, Xu J, Wan S X, Cheng X Q. Text matching as image recognition. In: Proceedings of the 30th AAAI Conference on Artificial Intelligence. 2016
|
[25] |
Nie Y F, Li Y, Nie J Y. Empirical study of multi-level convolution models for ir based on representations and interactions. In: Proceedings of the 2018 ACM SIGIR International Conference on Theory of Information Retrieval. 2018, 59–66
CrossRef
Google scholar
|
[26] |
Graves A, Schmidhuber J. Offline handwriting recognition with multidimensional recurrent neural networks. In: Proceedings of the 21st International Conference on Neural Information Processing Systems. 2009, 545–552
|
[27] |
Wu H C, Luk R W P, Wong K F, Kwok K L. A retrospective study of a hybrid document-context based retrieval model. Information Processing and Management, 2007, 43(5): 1308–1331
CrossRef
Google scholar
|
[28] |
Sutton R S, McAllester D A, Singh S P, Mansour Y. Policy gradient methods for reinforcement learning with function approximation. In: Proceedings of the 12th International Conference on Neural Information Process ing Systems. 2000, 1057–1063
|
[29] |
Zheng Y K, Fan Z, Liu Y Q, Luo C, Zhang M, Ma S P. Sogou-QCL: a new dataset with click relevance label. In: Proceedings of the 41st International ACM SIGIR Conference on Research and Development in Information Retrieval. 2018, 1117–1120
CrossRef
Google scholar
|
[30] |
Dehghani M, Zamani H, Severyn A, Kamps J, Croft W B. Neural ranking models with weak supervision. In: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2017, 65–74
CrossRef
Google scholar
|
[31] |
Wang C, Liu Y Q, Wang M, Zhou K, Nie J Y, Ma S P. Incorporating non-sequential behavior into click models. In: Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2015, 283–292
CrossRef
Google scholar
|
[32] |
Barrett M, Bingel J, Hollenstein N, Rei M, Søgaard A. Sequence classification with human attention. In: Proceedings of the 22nd Conference on Computational Natural Language Learning. 2018, 302–312
CrossRef
Google scholar
|
/
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