instruction:
In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
question:
While many recent advances in deep reinforcement learning rely on model-free methods, model-based approaches remain an alluring prospect for their potential to exploit unsupervised data to learn environment dynamics. One prospect is to pursue hybrid approaches, as in AlphaGo, which combines Monte-Carlo Tree Search (MCTS)—a model-based method—with deep-Q networks (DQNs)—a model-free method. MCTS requires generating rollouts, which is computationally expensive. In this paper, we propose to simulate roll-outs, exploiting the latest breakthroughs in image-to-image transduction, namely Pix2Pix GANs, to predict the dynamics of the environment. Our proposed algorithm, generative adversarial tree search (GATS), simulates rollouts up to a specified depth using both a GAN- based dynamics model and a reward predictor. GATS employs MCTS for planning over the simulated samples and uses DQN to estimate the Q-function at the leaf states. Our theoretical analysis establishes some favorable properties of GATS vis-a-vis the bias-variance trade-off and empirical results show that on 5 popular Atari games, the dynamics and reward predictors converge quickly to accurate solutions. However, GATS fails to outperform DQNs in 4 out of 5 games. Notably, in these experiments, MCTS has only short rollouts (up to tree depth 4), while previous successes of MCTS have involved tree depth in the hundreds. We present a hypothesis for why tree search with short rollouts can fail even given perfect modeling.
answer:
Surprising negative results on Model Based + Model deep RL


question:
  Analysis methods which enable us to better understand the
  representations and functioning of neural models of language are
  increasingly needed as deep learning becomes the dominant approach
  in NLP. Here we present two methods based on Representational
  Similarity Analysis (RSA) and Tree Kernels (TK) which allow us to
  directly quantify how strongly the information encoded in neural
  activation patterns corresponds to information represented by
  symbolic structures such as syntax trees. We first validate our
  methods on the case of a simple synthetic language for arithmetic
  expressions with clearly defined syntax and semantics, and show that
  they exhibit the expected pattern of results. We then apply our methods to
  correlate neural representations of English sentences with their
  constituency parse trees.
answer:
Two methods based on Representational Similarity Analysis (RSA) and Tree Kernels (TK) which directly quantify how strongly information encoded in neural activation patterns corresponds to information represented by symbolic structures.


question:
Labeled text classification datasets are typically only available in a few select languages. In order to train a model for e.g news categorization in a language $L_t$ without a suitable text classification dataset there are two options. The first option is to create a new labeled dataset by hand, and the second option is to transfer label information from an existing labeled dataset in a source language $L_s$ to the target language $L_t$. In this paper we propose a method for sharing label information across languages by means of a language independent text encoder. The encoder will give almost identical representations to multilingual versions of the same text. This means that labeled data in one language can be used to train a classifier that works for the rest of the languages. The encoder is trained independently of any concrete classification task and can therefore subsequently be used for any classification task.   We show that it is possible to obtain good performance even in the case where only a comparable corpus of texts is available.
answer:

Cross Language Text Classification by universal encoding