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Structural equation modeling of political discussion networks
This study conducts structural equation modeling (SEM) of political discussion networks. It examines multiple relationships between political discussion networks—network size and non-kin composition, political efficacy, and neighborhood conversation. Based on a two-step approach, it first analyzes and revises the measurement model and then analyzes and revises the structural model given the revised measurement model. The proposed SEM model includes ordered categorical variables as factor indicators in the confirmatory analysis and outcome variables in the structural regressions. Traditional estimation and regression methods need to be adjusted accordingly. This study uses WLS estimation and adopts a latent variable approach to study the categorical outcome variables in the SEM. The results show that the hypothesized SEM model is fully supported. Neighborhood conversation positively and directly contributes to political discussion network size as well as the non-kin composition of the networks. It also indirectly affects network size through political efficacy. Political efficacy also has a direct effect on network size.Statistic
Learning Discourse-level Diversity for Neural Dialog Models using Conditional Variational Autoencoders
While recent neural encoder-decoder models have shown great promise in
modeling open-domain conversations, they often generate dull and generic
responses. Unlike past work that has focused on diversifying the output of the
decoder at word-level to alleviate this problem, we present a novel framework
based on conditional variational autoencoders that captures the discourse-level
diversity in the encoder. Our model uses latent variables to learn a
distribution over potential conversational intents and generates diverse
responses using only greedy decoders. We have further developed a novel variant
that is integrated with linguistic prior knowledge for better performance.
Finally, the training procedure is improved by introducing a bag-of-word loss.
Our proposed models have been validated to generate significantly more diverse
responses than baseline approaches and exhibit competence in discourse-level
decision-making.Comment: Appeared in ACL2017 proceedings as a long paper. Correct a
calculation mistake in Table 1 E-bow & A-bow and results into higher score
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