Title: Learn from Your Neighbor: Learning Multi-modal Mappings from Sparse Annotations,
Abstract: Many structured prediction problems (particularly in vision and language
domains) are ambiguous, with multiple outputs being correct for an input - e.g.
there are many ways of describing an image, multiple ways of translating a
sentence; however, exhaustively annotating the applicability of all possible
outputs is intractable due to exponentially large output spaces (e.g. all
English sentences). In practice, these problems are cast as multi-class
prediction, with the likelihood of only a sparse set of annotations being
maximized - unfortunately penalizing for placing beliefs on plausible but
unannotated outputs. We make and test the following hypothesis - for a given
input, the annotations of its neighbors may serve as an additional supervisory
signal. Specifically, we propose an objective that transfers supervision from
neighboring examples. We first study the properties of our developed method in
a controlled toy setup before reporting results on multi-label classification
and two image-grounded sequence modeling tasks - captioning and question
generation. We evaluate using standard task-specific metrics and measures of
output diversity, finding consistent improvements over standard maximum
likelihood training and other baselines.