Takahiro Ishihara


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Reduction of Parameter Redundancy in Biaffine Classifiers with Symmetric and Circulant Weight Matrices
Tomoki Matsuno | Katsuhiko Hayashi | Takahiro Ishihara | Hitoshi Manabe | Yuji Matsumoto
Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation

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Neural Tensor Networks with Diagonal Slice Matrices
Takahiro Ishihara | Katsuhiko Hayashi | Hitoshi Manabe | Masashi Shimbo | Masaaki Nagata
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

Although neural tensor networks (NTNs) have been successful in many NLP tasks, they require a large number of parameters to be estimated, which often leads to overfitting and a long training time. We address these issues by applying eigendecomposition to each slice matrix of a tensor to reduce its number of paramters. First, we evaluate our proposed NTN models on knowledge graph completion. Second, we extend the models to recursive NTNs (RNTNs) and evaluate them on logical reasoning tasks. These experiments show that our proposed models learn better and faster than the original (R)NTNs.