Part 1. 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.
Part 2. Example
We propose a local coherence model based on a convolutional neural network that operates over the entity grid representation of a text. The model captures long range entity transitions along with entity-specific features without loosing generalization, thanks to the power of distributed representation. We present a pairwise ranking method to train the model in an end-to-end fashion on a task and learn task-specific high level features. Our evaluation on three different coherence assessment tasks demonstrates that our model achieves state of the art results outperforming existing models by a good margin.
Answer: A Neural Local Coherence Model
Explanation: This statement "A Neural Local Coherence Model" is taken from the subtext "convolutional neural network" and its explanation in the passage. This is a positive example because the title belongs to the mentions in the passage
Part 3. Exercise
In this work, we study an important problem: learning programs from input-output<lb>examples. We propose a novel method to learn a neural program operating a<lb>domain-specific non-differentiable machine, and demonstrate that this method<lb>can be applied to learn programs that are significantly more complex than the<lb>ones synthesized before: programming language parsers from input-output pairs<lb>without knowing the underlying grammar. The main challenge is to train the neural<lb>program without supervision on execution traces. To tackle it, we propose: (1)<lb>LL machines and neural programs operating them to effectively regularize the<lb>space of the learned programs; and (2) a two-phase reinforcement learning-based<lb>search technique to train the model. Our evaluation demonstrates that our approach<lb>can successfully learn to parse programs in both an imperative language and a<lb>functional language, and achieve 100% test accuracy, while existing approaches'<lb>accuracies are almost 0%. This is the first successful demonstration of applying<lb>reinforcement learning to train a neural program operating a non-differentiable<lb>machine that can fully generalize to test sets on a non-trivial task.
Answer:
Learning Neural Programs To Parse Programs