Abstract
Ad-hoc Retrieval based on deep learning model often suffers from the limitation of embedding semantic abuse problem. Inspired by the success of convolutional neural network based models in image processing, where a series of hidden layers extracts increasingly abstract features from a image, we propose a multidimensional interaction-focused model to solve the above problem in a image processing way. Firstly, we construct the query-document similarity matrix as a 3d tensor which means a word similarity value becomes a vector. Then we apply a CNN layer to capture complicated interaction patterns on every similarity chanel and a Bi-LSTM layer will map the output of CNN to a vector of fixed dimensionality. Finally a feed forward network will calculate a matching score. Experiments on the question-answer task with dataset WikiQA have achieved the state-of-the-art results compared to traditional statistical methods and deep neural network methods.
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References
Yu, L., Hermann, K.M., Blunsom12, P., Pulman, S.: Deep Learning for Answer Sentence Selection. arXiv preprint arXiv:1412.1632 (2014)
Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014)
Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Advances in Neural Information Processing Systems, pp. 3104–3112 (2014)
Guo, J., Fan, Y., Ai, Q., Croft, W.B.: A deep relevance matching model for ad-hoc retrieval. In: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, pp. 55–64. ACM (2016)
Reed, G.F., Lynn, F., Meade, B.D.: Use of coefficient of variation in assessing variability of quantitative assays. Clin. Diagn. Lab. Immunol. 9, 1235–1239 (2002)
Pang, L., Lan, Y., Guo, J., Xu, J., Wan, S., Cheng, X.: Text Matching as Image Recognition. In: AAAI, pp. 2793–2799 (2016)
Liu, T.-Y.: Learning to rank for information retrieval. INR 3, 225–331 (2009)
Yang, Y., Yih, W., Meek, C.: WikiQA: a challenge dataset for open-domain question answering. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp. 2013–2018 (2015)
Qin, T., Liu, T.-Y.: Introducing LETOR 4.0 datasets. arXiv preprint arXiv:1306.2597 (2013)
Zhai, C., Lafferty, J.: A study of smoothing methods for language models applied to ad hoc information retrieval. In: ACM SIGIR Forum, pp. 268–276. ACM (2017)
Huang, P.-S., He, X., Gao, J., 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 Conference on Information & Knowledge Management, pp. 2333–2338. ACM (2013)
Shen, Y., He, X., Gao, J., Deng, L., Mesnil, G.: Learning semantic representations using convolutional neural networks for web search. In: Proceedings of the 23rd International Conference on World Wide Web, pp. 373–374. ACM (2014)
Hu, B., Lu, Z., Li, H., Chen, Q.: Convolutional neural network architectures for matching natural language sentences. In: Advances in Neural Information Processing Systems, pp. 2042–2050 (2014)
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 International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 55–64. ACM (2017)
Järvelin, K., Kekäläinen, J.: Cumulated gain-based evaluation of IR techniques. ACM Trans. Inf. Syst. (TOIS) 20, 422–446 (2002)
Wan, S., Lan, Y., Guo, J., Xu, J., Pang, L., Cheng, X.: A deep architecture for semantic matching with multiple positional sentence representations. In: AAAI, pp. 2835–2841 (2016)
Socher, R., Huang, E.H., Pennin, J., Manning, C.D., Ng, A.Y.: Dynamic pooling and unfolding recursive autoencoders for paraphrase detection. In: Advances in Neural Information Processing Systems, pp. 801–809 (2011)
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. ACM (2017)
Pang, L., Lan, Y., Guo, J., Xu, J., Cheng, X.: A deep investigation of deep ir models. arXiv preprint arXiv:1707.07700 (2017)
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Sun, Q., Wu, J., Wu, Y. (2018). A Multidimensional Interaction-Focused Model for Ad-Hoc Retrieval. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11302. Springer, Cham. https://doi.org/10.1007/978-3-030-04179-3_23
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DOI: https://doi.org/10.1007/978-3-030-04179-3_23
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