Instructions: 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: This paper proposes DRL-Sensea multisense word representation learning model, to address the word sense ambiguity issue, where a sense selection module and a sense representation module are jointly learned in a reinforcement learning fashion. A novel reward passing procedure is proposed to enable joint training on the selection and representation modules. The modular design implements pure senselevel representation learning with linear time sense selection (decoding). We further develop a non-parametric learning algorithm and a sense exploration mechanism for better flexibility and robustness. The experiments on benchmark data show that the proposed approach achieves the state-of-the-art performance on contextual word similarities and comparable performance with Google's word2vec while using much less training data.
Output:
DRL-Sense: Deep Reinforcement Learning for Multi-Sense Word Representations