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
This report presents Giraffe, a chess engine that uses self-play to discover all its domain-specific knowledge, with minimal hand-crafted knowledge given by the programmer. Unlike previous attempts using machine learning only to perform parametertuning on hand-crafted evaluation functions, Giraffe's learning system also performs automatic feature extraction and pattern recognition. The trained evaluation function performs comparably to the evaluation functions of state-of-the-art chess engines all of which containing thousands of lines of carefully hand-crafted pattern recognizers, tuned over many years by both computer chess experts and human chess masters. Giraffe is the most successful attempt thus far at using end-to-end machine learning to play chess. We also investigated the possibility of using probability thresholds instead of depth to shape search trees. Depth-based searches form the backbone of virtually all chess engines in existence today, and is an algorithm that has become well-established over the past half century. Preliminary comparisons between a basic implementation of probability-based search and a basic implementation of depth-based search showed that our new probability-based approach performs moderately better than the established approach. There are also evidences suggesting that many successful ad-hoc add-ons to depth-based searches are generalized by switching to a probability-based search. We believe the probability-based search to be a more fundamentally correct way to perform minimax. Finally, we designed another machine learning system to shape search trees within the probability-based search framework. Given any position, this system estimates the probability of each of the moves being the best move without looking ahead. The system is highly effective the actual best move is within the top 3 ranked moves 70% of the time, out of an average of approximately 35 legal moves from each position. This also resulted in a significant increase in playing strength. With the move evaluator guiding a probability-based search using the learned evaluator, Giraffe plays at approximately the level of an FIDE International Master (top 2.2% of tournament chess players with an official rating)12. Federation Internationale des Echecs, or the World Chess Federation, is the international organisation that governs all major international chess competitions. Please see Appendix A for a description of the Elo rating system.
Answer:
Giraffe: Using Deep Reinforcement Learning to Play Chess