a neural representation of sketch drawings

Introduction

lalala

1. To provide a strong lower bound that any semi-supervised machine translation system is supposed to yield

Related Work

The authors offer two motivations for their work:

1. To translate between languages for which large parallel corpora does not exist

References

1. Bahdanau, Dzmitry, Kyunghyun Cho, and Yoshua Bengio. "Neural machine translation by jointly learning to align and translate." arXiv preprint arXiv:1409.0473 (2014).

fonts and examples

The unsupervised translation scheme has the following outline:

• The word-vector embeddings of the source and target languages are aligned in an unsupervised manner.
• Sentences from the source and target language are mapped to a common latent vector space by an encoder, and then mapped to probability distributions over

The objective function is the sum of:

1. The de-noising auto-encoder loss,

I shall describe these in the following sections.

From Conneau et al. (2017). The final row shows the performance of alignment method used in the present paper. Note the degradation in performance for more distant languages.

From the present paper. Results of an ablation study. Of note are the first, third, and forth rows, which demonstrate that while the translation component of the loss is relatively unimportant, the word vector alignment scheme and de-noising auto-encoder matter a great deal.