  The trigram `I love being' is expected to be followed by positive words such
as `happy'. In a sarcastic sentence, however, the word `ignored' may be
observed. The expected and the observed words are, thus, incongruous. We model
sarcasm detection as the task of detecting incongruity between an observed and
an expected word. In order to obtain the expected word, we use Context2Vec, a
sentence completion library based on Bidirectional LSTM. However, since the
exact word where such an incongruity occurs may not be known in advance, we
present two approaches: an All-words approach (which consults sentence
completion for every content word) and an Incongruous words-only approach
(which consults sentence completion for the 50% most incongruous content
words). The approaches outperform reported values for tweets but not for
discussion forum posts. This is likely to be because of redundant consultation
of sentence completion for discussion forum posts. Therefore, we consider an
oracle case where the exact incongruous word is manually labeled in a corpus
reported in past work. In this case, the performance is higher than the
all-words approach. This sets up the promise for using sentence completion for
sarcasm detection.
