  Many tasks in natural language understanding require learning relationships
between two sequences for various tasks such as natural language inference,
paraphrasing and entailment. These aforementioned tasks are similar in nature,
yet they are often modeled individually. Knowledge transfer can be effective
for closely related tasks. However, transferring all knowledge, some of which
irrelevant for a target task, can lead to sub-optimal results due to
\textit{negative} transfer. Hence, this paper focuses on the transferability of
both instances and parameters across natural language understanding tasks by
proposing an ensemble-based transfer learning method. \newline The primary
contribution of this paper is the combination of both \textit{Dropout} and
\textit{Bagging} for improved transferability in neural networks, referred to
as \textit{Dropping} herein. We present a straightforward yet novel approach
for incorporating source \textit{Dropping} Networks to a target task for
few-shot learning that mitigates \textit{negative} transfer. This is achieved
by using a decaying parameter chosen according to the slope changes of a
smoothed spline error curve at sub-intervals during training. We compare the
proposed approach against hard parameter sharing and soft parameter sharing
transfer methods in the few-shot learning case. We also compare against models
that are fully trained on the target task in the standard supervised learning
setup. The aforementioned adjustment leads to improved transfer learning
performance and comparable results to the current state of the art only using a
fraction of the data from the target task.
