  Product title compression for voice and mobile commerce is a well studied
problem with several supervised models proposed so far. However these models
have 2 major limitations; they are not designed to generate compressions
dynamically based on cues at inference time, and they do not transfer well to
different categories at test time. To address these shortcomings we model title
compression as a meta learning problem where we ask can we learn a title
compression model given only 1 example compression? We adopt an unsupervised
approach to meta training by proposing an automatic task generation algorithm
that models the observed label generation process as the outcome of 4
unobserved processes. We create parameterized approximations to each of these 4
latent processes to get a principled way of generating random compression
rules, which are treated as different tasks. For our main meta learner, we use
2 models; M1 and M2. M1 is a task agnostic embedding generator whose output
feeds into M2 which is a task specific label generator. We pre-train M1 on a
novel unsupervised segment rank prediction task that allows us to treat M1 as a
segment generator that also learns to rank segments during the meta-training
process. Our experiments on 16000 crowd generated meta-test examples show that
our unsupervised meta training regime is able to acquire a learning algorithm
for different tasks after seeing only 1 example for each task. Further, we show
that our model trained end to end as a black box meta learner, outperforms non
parametric approaches. Our best model obtains an F1 score of 0.8412, beating
the baseline by a large margin of 25 F1 points.
