Quantum Language Model with Entanglement Embedding for Question Answering
This work addresses the challenge of fully exploiting quantum probabilistic descriptions for natural language processing, specifically in question answering, representing an incremental improvement over existing quantum language models.
The authors tackled the problem of modeling word sequences in quantum language models by proposing a novel Entanglement Embedding module to capture non-classical correlations, resulting in superior performance on Question Answering datasets compared to classical and other quantum models.
Quantum Language Models (QLMs) in which words are modelled as quantum superposition of sememes have demonstrated a high level of model transparency and good post-hoc interpretability. Nevertheless, in the current literature word sequences are basically modelled as a classical mixture of word states, which cannot fully exploit the potential of a quantum probabilistic description. A full quantum model is yet to be developed to explicitly capture the non-classical correlations within the word sequences. We propose a neural network model with a novel Entanglement Embedding (EE) module, whose function is to transform the word sequences into entangled pure states of many-body quantum systems. Strong quantum entanglement, which is the central concept of quantum information and an indication of parallelized correlations among the words, is observed within the word sequences. Numerical experiments show that the proposed QLM with EE (QLM-EE) achieves superior performance compared with the classical deep neural network models and other QLMs on Question Answering (QA) datasets. In addition, the post-hoc interpretability of the model can be improved by quantizing the degree of entanglement among the words.