instruction:
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.
question:
Words can be represented by composing the representations of subword units such as word segments, characters, and/or character n-grams. While such representations are effective and may capture the morphological regularities of words, they have not been systematically compared, and it is not understood how they interact with different morphological typologies. On a language modeling task, we present experiments that systematically vary (1) the basic unit of representation, (2) the composition of these representations, and (3) the morphological typology of the language modeled. Our results largely confirm previous findings that character representations are effective across many languages, though we find that a previously unstudied combination of character trigram representations composed with bi-LSTMs outperforms most other settings. However, we also find room for improvement: character models do not match the predictive accuracy of a model with access to explicit morphological analyses.
answer:
From Characters to Words to in Between: Do We Capture Morphology?

question:
The Kaldi 1 toolkit is becoming popular for constructing automated speech recognition (ASR) systems. Meanwhile, in recent years, deep neural networks (DNNs) have shown state-of-the-art performance on various ASR tasks. This document describes our recipes to implement fully-fledged DNN acoustic modeling using Kaldi and PDNN. PDNN is a lightweight deep learning toolkit developed under the Theano environment. Using these recipes, we can build up multiple systems including DNN hybrid systems, convolutional neural network (CNN) systems and bottleneck feature systems. These recipes are directly based on the Kaldi Switchboard 110-hour setup. However, adapting them to new datasets is easy to achieve.
answer:
Kaldi+PDNN: Building DNN-based ASR Systems with Kaldi and PDNN

question:
Suppose we are given the conditional proba bility of one variable given some other vari ables. Normally the full joint distribution over the conditioning variables is required to determine the probability of the conditioned variable. Under what circumstances are the marginal distributions over the conditioning variables sufficient to determine the probabil ity of the conditioned variable? Sufficiency in this sense is equivalent to additive separa bility of the conditional probability distribu tion. Such separability structure is natural and can be exploited for efficient inference. Separability has a natural generalization to conditional separability. Separability provides a precise notion of hi erarchical decomposition in temporal prob abilistic models. Given a system that is decomposed into separable subsystems, ex act marginal probabilities over subsystems at future points in time can be computed by propagating marginal subsystem probabil ities, rather than complete system joint prob abilities. Thus, separability can make exact prediction tractable. However, observations can break separability, so exact monitoring of dynamic systems remains hard.
answer:
Sufficiency, Separability and Temporal Probabilistic Models