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.

Q: A generally intelligent learner should generalize to more complex tasks than it has previously encountered, but the two common paradigms in machine learning -- either training a separate learner per task or training a single learner for all tasks -- both have difficulty with such generalization because they do not leverage  the compositional structure of the task distribution. This paper introduces the compositional problem graph as a broadly applicable formalism to relate tasks of different complexity in terms of problems with shared subproblems. We propose the compositional generalization problem for measuring how readily old knowledge can be reused and hence built upon. As a first step for tackling compositional generalization, we introduce the compositional recursive learner, a domain-general framework for learning algorithmic procedures for composing representation transformations, producing a learner that reasons about what computation to execute by making analogies to previously seen problems. We show on a symbolic and a high-dimensional domain that our compositional approach can generalize to more complex problems than the learner has previously encountered, whereas baselines that are not explicitly compositional do not.

A: We explore the problem of compositional generalization and propose a means for endowing neural network architectures with the ability to compose themselves to solve these problems.
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Q: A significant challenge for the practical application of reinforcement learning toreal world problems is the need to specify an oracle reward function that correctly defines a task. Inverse reinforcement learning (IRL) seeks to avoid this challenge by instead inferring a reward function from expert behavior.   While appealing, it can be impractically expensive to collect datasets of demonstrations that cover the variation common in the real world (e.g. opening any type of door). Thus in practice, IRL must commonly be performed with only a limited set of demonstrations where it can be exceedingly difficult to unambiguously recover a reward function. In this work, we exploit the insight that demonstrations from other tasks can be used to constrain the set of possible reward functions by learning a "prior" that is specifically optimized for the ability to infer expressive reward functions from limited numbers of demonstrations.   We demonstrate that our method can efficiently recover rewards from images for novel tasks and provide intuition as to how our approach is analogous to learning a prior.

A: The applicability of inverse reinforcement learning is often hampered by the expense of collecting expert demonstrations; this paper seeks to broaden its applicability by incorporating prior task information through meta-learning.
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Q: Chinese text classification has received more and more attention today. However, the problem of Chinese text representation still hinders the improvement of Chinese text classification, especially the polyphone and the homophone in social media. To cope with it effectively, we propose a new structure, the Extractor, based on attention mechanisms and design novel attention networks named Extractor-attention network (EAN). Unlike most of previous works, EAN uses a combination of a word encoder and a Pinyin character encoder instead of a single encoder. It improves the capability of Chinese text representation. Moreover, compared with the hybrid encoder methods, EAN has more complex combination architecture and more reducing parameters structures. Thus, EAN can take advantage of a large amount of information that comes from multi-inputs and alleviates efficiency issues. The proposed model achieves the state of the art results on 5 large datasets for Chinese text classification.

A:
We propose a novel attention networks with the hybird encoder to solve the text representation issue of Chinese text classification, especially the language phenomena about pronunciations such as the polyphone and the homophone.
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