Definition: In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.
Input: Domain similarity measures can be used<lb>to gauge adaptability and select suitable<lb>data for transfer learning, but existing ap-<lb>proaches define ad hoc measures that are deemed suitable for respective tasks. In-<lb>spired by work on curriculum learning, we<lb>propose to learn data selection measures<lb>using Bayesian Optimization and evaluate<lb>them across models, domains and tasks. Our learned measures outperform existing<lb>domain similarity measures significantly<lb>on three tasks: sentiment analysis, part-<lb>of-speech tagging, and parsing. We show<lb>the importance of complementing similarity with diversity, and that learned mea-<lb>sures are—to some degree—transferable<lb>across models, domains, and even tasks.

Learning to select data for transfer learning with Bayesian Optimization