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.

Example Input: Abstraction of Markov Decision Processes is a useful tool for solving complex problems, as it can ignore unimportant aspects of an environment, simplifying the process of learning an optimal policy. In this paper, we propose a new algorithm for finding abstract MDPs in environments with continuous state spaces. It is based on MDP homomorphisms, a structure-preserving mapping between MDPs. We demonstrate our algorithm's ability to learns abstractions from collected experience and show how to reuse the abstractions to guide exploration in new tasks the agent encounters. Our novel task transfer method beats a baseline based on a deep Q-network.
Example Output: We create abstract models of environments from experience and use them to learn new tasks faster.

Example Input: 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.
Example Output: 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.

Example Input: Text generation is ubiquitous in many NLP tasks, from summarization, to dialogue and machine translation. The dominant parametric approach is based on locally normalized models which predict one word at a time. While these work remarkably well, they are plagued by exposure bias due to the greedy nature of the generation process. In this work, we investigate un-normalized energy-based models (EBMs) which operate not at the token but at the sequence level. In order to make training tractable, we first work in the residual of a pretrained locally normalized language model and second we train using noise contrastive estimation. Furthermore, since the EBM works at the sequence level, we can leverage pretrained bi-directional contextual representations, such as BERT and RoBERTa. Our experiments on two large language modeling datasets show that residual EBMs yield lower perplexity compared to locally normalized baselines. Moreover, generation via importance sampling is very efficient and of higher quality than the baseline models according to human evaluation.
Example Output:
We show that Energy-Based models when trained on the residual of an auto-regressive language model can be used effectively and efficiently to generate text.