  The explosion of user-generated content (UGC)--e.g. social media posts,
comments, and reviews--has motivated the development of NLP applications
tailored to these types of informal texts. Prevalent among these applications
have been sentiment analysis and machine translation (MT). Grounded in the
observation that UGC features highly idiomatic, sentiment-charged language, we
propose a decoder-side approach that incorporates automatic sentiment scoring
into the MT candidate selection process. We train separate English and Spanish
sentiment classifiers, then, using n-best candidates generated by a baseline MT
model with beam search, select the candidate that minimizes the absolute
difference between the sentiment score of the source sentence and that of the
translation, and perform a human evaluation to assess the produced
translations. Unlike previous work, we select this minimally divergent
translation by considering the sentiment scores of the source sentence and
translation on a continuous interval, rather than using e.g. binary
classification, allowing for more fine-grained selection of translation
candidates. The results of human evaluations show that, in comparison to the
open-source MT baseline model on top of which our sentiment-based pipeline is
built, our pipeline produces more accurate translations of colloquial,
sentiment-heavy source texts.
