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.

Ex Input:
We introduce a model for constructing vector representations of words by composing characters using bidirectional LSTMs. Relative to traditional word representation models that have independent vectors for each word type, our model requires only a single vector per character type and a fixed set of parameters for the compositional model. Despite the compactness of this model and, more importantly, the arbitrary nature of the form–function relationship in language, our “composed” word representations yield state-of-the-art results in language modeling and part-of-speech tagging. Benefits over traditional baselines are particularly pronounced in morphologically rich languages (e.g., Turkish).

Ex Output:
Finding Function in Form: Compositional Character Models for Open Vocabulary Word Representation


Ex Input:
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.

Ex Output:
Learning Finite-State Controllers for Partially Observable Environments


Ex Input:
We present a novel approach to constraintbased causal discovery, that takes the form of straightforward logical inference, applied to a list of simple, logical statements about causal relations that are derived directly from observed (in)dependencies. It is both sound and complete, in the sense that all invariant features of the corresponding partial ancestral graph (PAG) are identified, even in the presence of latent variables and selection bias. The approach shows that every identifiable causal relation corresponds to one of just two fundamental forms. More importantly, as the basic building blocks of the method do not rely on the detailed (graphical) structure of the corresponding PAG, it opens up a range of new opportunities, including more robust inference, detailed accountability, and application to large models.

Ex Output:

A Logical Characterization of Constraint-Based Causal Discovery