  Answering a programming question using only its title is difficult as salient
contextual information is omitted. Based on this observation, we present a
corpus of over 40,000 StackOverflow question texts to be used in conjunction
with their corresponding intents from the CoNaLa dataset (Yin et al., 2018).
Using both the intent and question body, we use BART to establish a baseline
BLEU score of 34.35 for this new task. We find further improvements of $2.8\%$
by combining the mined CoNaLa data with the labeled data to achieve a 35.32
BLEU score. We evaluate prior state-of-the-art CoNaLa models with this
additional data and find that our proposed method of using the body and mined
data beats the BLEU score of the prior state-of-the-art by $71.96\%$. Finally,
we perform ablations to demonstrate that BART is an unsupervised multimodal
learner and examine its extractive behavior. The code and data can be found
https://github.com/gabeorlanski/stackoverflow-encourages-cheating.
