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.
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Question: Reactive (memoryless) policies are sufficient in completely observable Markov decision pro­ cesses (MDPs), but some kind of memory is usually necessary for optimal control of a par­ tially observable MDP. Policies with finite mem­ ory can be represented as finite-state automata. In this paper, we extend Baird and Moore's YAPS algorithm to the problem of learning gen­ eral finite-state automata. Because it performs stochastic gradient descent, this algorithm can be shown to converge to a locally optimal finite­ state controller. We provide the details of the algorithm and then consider the question of un­ der what conditions stochastic gradient descent will outperform exact gradient descent. We con­ clude with empirical results comparing the per­ formance of stochastic and exact gradient de­ scent, and showing the ability of our algorithm to extract the useful information contained in the sequence of past observations to compensate for the lack of observability at each time-step.

Answer: Learning Finite-State Controllers for Partially Observable Environments


Question: Determining semantic textual similarity is a core research subject in natural language processing. Since vector-based models for sentence representation often use shallow information, capturing accurate semantics is difficult. By contrast, logical semantic representations capture deeper levels of sentence semantics, but their symbolic nature does not offer graded notions of textual similarity. We propose a method for determining semantic textual similarity by combining shallow features with features extracted from natural deduction proofs of bidirectional entailment relations between sentence pairs. For the natural deduction proofs, we use ccg2lambda, a higherorder automatic inference system, which converts Combinatory Categorial Grammar (CCG) derivation trees into semantic representations and conducts natural deduction proofs. Experiments show that our system was able to outperform other logicbased systems and that features derived from the proofs are effective for learning textual similarity.

Answer: Determining Semantic Textual Similarity using Natural Deduction Proofs


Question: A crucial aspect of a knowledge base population system that extracts new facts from text corpora, is the generation of training data for its relation extractors. In this paper, we present a method that maximizes the effectiveness of newly trained relation extractors at a minimal annotation cost. Manual labeling can be significantly reduced by Distant Supervision, which is a method to construct training data automatically by aligning a large text corpus with an existing knowledge base of known facts. For example, all sentences mentioning both ‘Barack Obama’ and ‘US’ may serve as positive training instances for the relation born in(subject,object). However, distant supervision typically results in a highly noisy training set: many training sentences do not really express the intended relation. We propose to combine distant supervision with minimal manual supervision in a technique called feature labeling, to eliminate noise from the large and noisy initial training set, resulting in a significant increase of precision. We further improve on this approach by introducing the Semantic Label Propagation method, which uses the similarity between low-dimensional representations of candidate training instances, to extend the training set in order to increase recall while maintaining high precision. Our proposed strategy for generating training data is studied and evaluated on an established test collection designed for knowledge base population tasks. The experimental results show that the Semantic Label Propagation strategy leads to substantial performance gains when compared to existing approaches, while requiring an almost negligible manual annotation effort.

Answer:
Knowledge Base Population using Semantic Label Propagation