  While the way intermediate representations are generated in encoder-decoder
sequence-to-sequence models typically allow them to preserve the semantics of
the input sentence, input features such as formality might be left out. On the
other hand, downstream tasks such as translation would benefit from working
with a sentence representation that preserves formality in addition to
semantics, so as to generate sentences with the appropriate level of social
formality -- the difference between speaking to a friend versus speaking with a
supervisor. We propose a sequence-to-sequence method for learning a
formality-aware representation for Japanese sentences, where sentence
generation is conditioned on both the original representation of the input
sentence, and a side constraint which guides the sentence representation
towards preserving formality information. Additionally, we propose augmenting
the sentence representation with a learned representation of formality which
facilitates the extraction of formality in downstream tasks. We address the
lack of formality-annotated parallel data by adapting previous works on
procedural formality classification of Japanese sentences. Experimental results
suggest that our techniques not only helps the decoder recover the formality of
the input sentence, but also slightly improves the preservation of input
sentence semantics.
