Sosuke Kobayashi

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We propose a novel neural network model for machine reading, DER Network, which explicitly implements a reader building dynamic meaning representations for entities by gathering and accumulating information around the entities as it reads a document. Evaluated on a recent large scale dataset (Hermann et al., 2015), our model exhibits better results than(More)
The Story Cloze Test consists of choosing a sentence that best completes a story given two choices. In this paper we present a system that performs this task using a supervised binary classifier on top of a recurrent neural network to predict the probability that a given story ending is correct. The classifier is trained to distinguish correct story endings(More)
In this paper, we compare feature-based and Neural Network-based approaches on the supervised stance classification task for tweets in SemEval-2016 Task 6 Subtask A (Mohammad et al., 2016). In the feature-based approach, we use external resources such as lexicons and crawled texts. The Neural Network based approach employs Convolutional Neural Network(More)
This study addresses the problem of identifying the meaning of unknown words or entities in a discourse with respect to the word embedding approaches used in neural language models. We proposed a method for on-the-fly construction and exploitation of word embeddings in both the input and output layers of a neural model by tracking contexts. This extends the(More)
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