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https://www.selleckchem.com/products/stat-in-1.html Firstly, we extract predecessor paths of head entities and connection paths between each entity pair. Then, a hierarchical attention mechanism is designed to capture the information of different granularities, including entity/relation-level and path-level features. Finally, multi-granularity features are fused together to predict the right answers. We go one step further to select the most significant path as the explanation for predicted answers. Comprehensive experimental results demonstrate that our method achieves competitive performance compared with the baselines on three benchmark datasets.End-to-end TTS advancement has shown that synthesized speech prosody can be controlled by conditioning the decoder with speech prosody attribute labels. However, to annotate quantitatively the prosody patterns of a large set of training data is both time consuming and expensive. To use unannotated data, variational autoencoder (VAE) has been proposed to model individual prosody attribute as a random variable in the latent space. The VAE is an unsupervised approach and the corresponding latent variables are in general correlated with each other. For more effective and direct control of speech prosody along each attribute dimension, it is highly desirable to disentangle the correlated latent variables. Additionally, being able to interpret the disentangled attributes as speech perceptual cues is useful for designing more efficient prosody control of TTS. In this paper, we propose two attribute separation schemes (1) using 3 separate VAEs to model the real-valued, different prosodic features, i.e., F0, energy and duration; (2) minimizing mutual information between different prosody attributes to remove their mutual correlations, for facilitating more direct prosody control. Experimental results confirm that the two proposed schemes can indeed make individual prosody attributes more interpretable and direct TTS prosody contro

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