@article{(Open Science Index):https://publications.waset.org/pdf/10011848,
	  title     = {Variational Explanation Generator: Generating Explanation for Natural Language Inference Using Variational Auto-Encoder},
	  author    = {Zhen Cheng and  Xinyu Dai and  Shujian Huang and  Jiajun Chen},
	  country	= {},
	  institution	= {},
	  abstract     = {Recently, explanatory natural language inference has
attracted much attention for the interpretability of logic relationship
prediction, which is also known as explanation generation for
Natural Language Inference (NLI). Existing explanation generators
based on discriminative Encoder-Decoder architecture have achieved
noticeable results. However, we find that these discriminative
generators usually generate explanations with correct evidence but
incorrect logic semantic. It is due to that logic information is
implicitly encoded in the premise-hypothesis pairs and difficult
to model. Actually, logic information identically exists between
premise-hypothesis pair and explanation. And it is easy to extract
logic information that is explicitly contained in the target explanation.
Hence we assume that there exists a latent space of logic information
while generating explanations. Specifically, we propose a generative
model called Variational Explanation Generator (VariationalEG) with
a latent variable to model this space. Training with the guide
of explicit logic information in target explanations, latent variable
in VariationalEG could capture the implicit logic information in
premise-hypothesis pairs effectively. Additionally, to tackle the
problem of posterior collapse while training VariaztionalEG, we
propose a simple yet effective approach called Logic Supervision on
the latent variable to force it to encode logic information. Experiments
on explanation generation benchmark—explanation-Stanford Natural
Language Inference (e-SNLI) demonstrate that the proposed
VariationalEG achieves significant improvement compared to
previous studies and yields a state-of-the-art result. Furthermore, we
perform the analysis of generated explanations to demonstrate the
effect of the latent variable.},
	    journal   = {International Journal of Computer and Information Engineering},
	  volume    = {15},
	  number    = {2},
	  year      = {2021},
	  pages     = {119 - 125},
	  ee        = {https://publications.waset.org/pdf/10011848},
	  url   	= {https://publications.waset.org/vol/170},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 170, 2021},
	}