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A Deep Decomposable Model for Disentangling Syntax and Semantics in Sentence Representation

نموذج متحلل عميق ل Secornangling بناء الجملة والدلالات في تمثيل الجملة

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 Publication date 2021
and research's language is English
 Created by Shamra Editor




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Recently, disentanglement based on a generative adversarial network or a variational autoencoder has significantly advanced the performance of diverse applications in CV and NLP domains. Nevertheless, those models still work on coarse levels in the disentanglement of closely related properties, such as syntax and semantics in human languages. This paper introduces a deep decomposable model based on VAE to disentangle syntax and semantics by using total correlation penalties on KL divergences. Notably, we decompose the KL divergence term of the original VAE so that the generated latent variables can be separated in a more clear-cut and interpretable way. Experiments on benchmark datasets show that our proposed model can significantly improve the disentanglement quality between syntactic and semantic representations for semantic similarity tasks and syntactic similarity tasks.

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This research shows the concept of sentence syntax and the text syntax and the difference between them, beside their respective areas .It also tries to specify the obstacles which prevent the progress of this kind of linguistic lesson in our Arabi an collages .Then it stops at the trends of linguistic studies where such kind of linguistic lesson appears .Also tries to monitor the reality of this lingual lesson in the Syrian collages through one sample ,that is Al Baath University .Finally finishes by the most important recommendations which can contribute in developing this kind of lingual lesson .

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