Morphological Inflection Generation with Multi-space Variational Encoder-Decoders
نویسندگان
چکیده
This paper describes the CMU submission to shared task 1 of SIGMORPHON 2017. The system is based on the multi-space variational encoder-decoder (MSVED) method of Zhou and Neubig (2017), which employs both continuous and discrete latent variables for the variational encoder-decoder and is trained in a semi-supervised fashion. We discuss some language-specific errors and present result analysis.
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