produces a larger fraction of novel sentences than an RNN trained on a single caption domain. A sentence is novel if there is no match in the MSCOCO training set or the sentiment caption dataset. Overall, [SentiCap]{} produces 95.7% novel captions; while [CNN+RNN]{}, which was trained only on MSCOCO, produces 38.2% novel captions – higher than the 20% observed in [@vinyals2015show].

Figure \[fig:exp\] contains a number of examples with generated sentiment captions – the left half are positive, the right half negative. We can see that the switch variable captures almost all sentiment phrases, and some of the surrounding words (e.g. [*train station*]{}, [*plate*]{}). Examples in the first two rows are generally descriptive and accurate such as [*delicious piece of cake*]{} (2a), [*ugly car*]{} and [*abandoned buildings*]{} (1c). Results for the other examples contain more or less inappropriateness in either the content description or sentiment, or both. (3b) captures the [*happy*]{} spirit correctly, but the semantic of a child in playground is mistaken with that of a man on a skateboard due to very high visual resemblance. [(3d) interestingly juxtaposed the positive ANP [*clever trick*]{} and negative ANP [*dead man*]{}, creating an impossible yet amusing caption. ]{}

Conclusion
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\[sec:conclusion\]

