Abstract
Bi-directional LSTM (BLSTM) often utilizes Attention Mechanism (AM) to improve the ability of modeling sentences. But additional parameters within AM may lead to difficulties of model selection and BLSTM training. To solve the problem, this paper redefines AM from a novel perspective of the quantum cognition and proposes a parameter-free Quantum AM (QAM). Furthermore, we make a quantum interpretation for BLSTM with Two-State Vector Formalism (TSVF) and find the similarity between sentence understanding and quantum Weak Measurement (WM) under TSVF. Weak value derived from WM is employed to represent the attention for words in a sentence. Experiments show that QAM based BLSTM outperforms common AM (CAM) [1] based BLSTM on most classification tasks discussed in this paper.
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Notes
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In QT, the quantum probability space is encapsulated in an Hilbert space \(\mathbb {H}^n\), which is an abstract vector space processing the structure of the inner product. A finite dimensional space is sufficient for the work reported in this paper. Thus, we limit our researches to a finite real space \(\mathbb {R}^n\). With the Dirac’s notation, a quantum state can be written as a column vector \(| \varPsi \rangle \), whose conjugate transpose is a row vector\(\langle \varPsi |\).
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http://nlp.stanford.edu/sentiment/ Data is actually provided at the phrase level. Hence both phrases and sentences are used to train the model, but only sentences are scored at test time [2, 4, 16]. Thus the training set is an order of magnitude larger than listed in Table 1.
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Acknowledgments
This work is funded in part by the Chinese 863 Program (grant No. 2015AA015403), the Key Project of Tianjin Natural Science Foundation (grant No. 15JCZDJC31100), the Tianjin Younger Natural Science Foundation (Grant no: 14JCQNJC00400), the Major Project of Chinese National Social Science Fund (grant No. 14ZDB153) and MSCA-ITN-ETN - European Training Networks Project (grant No. 721321, QUARTZ).
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Niu, X., Hou, Y., Wang, P. (2017). Bi-Directional LSTM with Quantum Attention Mechanism for Sentence Modeling. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10635. Springer, Cham. https://doi.org/10.1007/978-3-319-70096-0_19
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