Grammatically-Interpretable Learned Representations in Deep NLP Models - Palangi, Hamid; Huang, Qiuyuan; Smolensky, Paul; He, Xiaodong; Deng, Li (NIPS 2017, Workshop, 2017)
We introduce two architectures, the Tensor Product Recurrent Network (TPRN) and the Tensor Product Generation Network (TPGN). In the application of TPRN, internal representations — learned by end-to-end optimization in a deep neural network performing a textual QA task — are interpretable using basic concepts from linguistic theory. This interpretability is achieved without paying a performance penalty. In another application, image-to-text generation or image captioning, TPGN gives better results than the state-of-the-art long short-term memory (LSTM) based approaches. Learned internal representations in the TPGN can also be interpreted as containing grammatical-role information.