1 research outputs found
Concept Matching for Low-Resource Classification
We propose a model to tackle classification tasks in the presence of very
little training data. To this aim, we approximate the notion of exact match
with a theoretically sound mechanism that computes a probability of matching in
the input space. Importantly, the model learns to focus on elements of the
input that are relevant for the task at hand; by leveraging highlighted
portions of the training data, an error boosting technique guides the learning
process. In practice, it increases the error associated with relevant parts of
the input by a given factor. Remarkable results on text classification tasks
confirm the benefits of the proposed approach in both balanced and unbalanced
cases, thus being of practical use when labeling new examples is expensive. In
addition, by inspecting its weights, it is often possible to gather insights on
what the model has learned