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- W2899420756 abstract "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." @default.
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- W2899420756 date "2017-12-04" @default.
- W2899420756 modified "2023-09-24" @default.
- W2899420756 title "Grammatically-Interpretable Learned Representations in Deep NLP Models" @default.
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