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]: Rapid crisis response requires real-time analysis of messages. After a disaster happens, volunteers attempt to classify tweets to determine needs, e.g., supplies, infrastructure damage, etc. Given labeled data, supervised machine learning can help classify these messages. Scarcity of labeled data causes poor performance in machine training. Can we reuse old tweets to train classifiers? How can we choose labeled tweets for training? Specifically, we study the usefulness of labeled data of past events. Do labeled tweets in different language help? We observe the performance of our classifiers trained using different combinations of training sets obtained from past disasters. We perform extensive experimentation on real crisis datasets and show that the past labels are useful when both source and target events are of the same type (e.g. both earthquakes). For similar languages (e.g., Italian and Spanish), cross-language domain adaptation was useful, however, when for different languages (e.g., Italian and English), the performance decreased.
[A]: Cross-Language Domain Adaptation for Classifying Crisis-Related Short Messages

[Q]: Recent research shows that deep neural networks (DNNs) can be used to extract deep speaker vectors (d-vectors) that preserve speaker characteristics and can be used in speaker verification. This new method has been tested on text-dependent speaker verification tasks, and improvement was reported when combined with the conventional i-vector method. This paper extends the d-vector approach to semi textindependent speaker verification tasks, i.e., the text of the speech is in a limited set of short phrases. We explore various settings of the DNN structure used for d-vector extraction, and present a phone-dependent training which employs the posterior features obtained from an ASR system. The experimental results show that it is possible to apply d-vectors on semi text-independent speaker recognition, and the phone-dependent training improves system performance.
[A]: Deep Speaker Vectors for Semi Text-independent Speaker Verification

[Q]: The efficiency of algorithms using sec ondary structures for probabilistic inference in Bayesian networks can be improved by ex ploiting independence relations induced by evidence and the direction of the links in the original network. In this paper we present an algorithm that on-line exploits indepen dence relations induced by evidence and the direction of the links in the original network to reduce both time and space costs. In stead of multiplying the conditional proba bility distributions for the various cliques, we determine on-line which potentials to multi ply when a message is to be produced. The performance improvement of the algorithm is emphasized through empirical evaluations in volving large real world Bayesian networks, and we compare the method with the HUGIN and Shafer-Shenoy inference algorithms.
[A]:
Lazy Propagation in Junction Trees