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.

Q: Domain adaptation is important in sentiment analysis as sentiment-indicating words vary between domains. Recently, multi-domain adaptation has become more pervasive, but existing approaches train on all available source domains including dissimilar ones. However, the selection of appropriate training data is as important as the choice of algorithm. We undertake to our knowledge for the first time an extensive study of domain similarity metrics in the context of sentiment analysis and propose novel representations, metrics, and a new scope for data selection. We evaluate the proposed methods on two largescale multi-domain adaptation settings on tweets and reviews and demonstrate that they consistently outperform strong random and balanced baselines, while our proposed selection strategy outperforms instance-level selection and yields the best score on a large reviews corpus. All experiments are available at url_redacted1

A: Data Selection Strategies for Multi-Domain Sentiment Analysis
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Q: 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.

A: Kaldi+PDNN: Building DNN-based ASR Systems with Kaldi and PDNN
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Q: A central task in many applications is reasoning about processes that change over continuous time. Recently, Nodelman et al. introduced continuous time Bayesian networks (CTBNs), a structured representation for representing Continuous Time Markov Processes over a structured state space. In this paper, we introduce continuous time Markov networks (CTMNs), an alternative representation language that represents a different type of continuous-time dynamics, particularly appropriate for modeling biological and chemical systems. In this language, the dynamics of the process is described as an interplay between two forces: the tendency of each entity to change its state, which we model using a continuous-time proposal process that suggests possible local changes to the state of the system at different rates; and a global fitness or energy function of the entire system, governing the probability that a proposed change is accepted, which we capture by a Markov network that encodes the fitness of different states. We show that the fitness distribution is also the stationary distribution of the Markov process, so that this representation provides a characterization of a temporal process whose stationary distribution has a compact graphical representation. We describe the semantics of the representation, its basic properties, and how it compares to CTBNs. We also provide an algorithm for learning such models from data, and demonstrate its potential benefit over other learning approaches.

A:
Continuous Time Markov Networks
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