CNM: An Interpretable Complex-valued Network for Matching
This is an incremental approach for NLP researchers, offering interpretability in semantic matching tasks.
The paper tackles semantic matching in natural language processing by modeling language using quantum physics, resulting in a complex-valued network (CNM) that achieves performance comparable to CNN and RNN baselines on two QA datasets.
This paper seeks to model human language by the mathematical framework of quantum physics. With the well-designed mathematical formulations in quantum physics, this framework unifies different linguistic units in a single complex-valued vector space, e.g. words as particles in quantum states and sentences as mixed systems. A complex-valued network is built to implement this framework for semantic matching. With well-constrained complex-valued components, the network admits interpretations to explicit physical meanings. The proposed complex-valued network for matching (CNM) achieves comparable performances to strong CNN and RNN baselines on two benchmarking question answering (QA) datasets.