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• W2898996181 abstract "Widely used recurrent units, including Long-short Term Memory (LSTM) and Gated Recurrent Unit (GRU), perform well on natural language tasks, but their ability to learn structured representations is still questionable. Exploiting Tensor Product Representations (TPRs) --- distributed representations of symbolic structure in which vector-embedded symbols are bound to vector-embedded structural positions --- we propose the TPRU, a recurrent unit that, at each time step, explicitly executes structural-role binding and unbinding operations to incorporate structural information into learning. Experiments are conducted on both the Logical Entailment task and the Multi-genre Natural Language Inference (MNLI) task, and our TPR-derived recurrent unit provides strong performance with significantly fewer parameters than LSTM and GRU baselines. Furthermore, our learnt TPRU trained on MNLI demonstrates solid generalisation ability on downstream tasks." @default.
• W2898996181 created "2018-11-09" @default.
• W2898996181 creator A5071129405 @default.
• W2898996181 creator A5078551585 @default.
• W2898996181 creator A5088217655 @default.
• W2898996181 date "2018-10-29" @default.
• W2898996181 modified "2023-09-27" @default.
• W2898996181 title "Learning Distributed Representations of Symbolic Structure Using Binding and Unbinding Operations." @default.
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• W2898996181 hasPublicationYear "2018" @default.
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