Bi-Directional LSTM with Quantum Attention Mechanism for Sentence Modeling

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.