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: This paper presents generalized probabilistic models for high-order projective dependency parsing and an algorithmic framework for learning these statistical models involving dependency trees. Partition functions and marginals for high-order dependency trees can be computed efficiently, by adapting our algorithms which extend the inside-outside algorithm to higher-order cases. To show the effectiveness of our algorithms, we perform experiments on three languages English, Chinese and Czech, using maximum conditional likelihood estimation for model training and L-BFGS for parameter estimation. Our methods achieve competitive performance for English, and outperform all previously reported dependency parsers for Chinese and Czech.

Answer: Probabilistic Models for High-Order Projective Dependency Parsing

Question: In recent years, Deep Learning has become the go-to solution for a broad range of applications, often outperforming state-of-the-art. However, it is important, for both theoreticians and practitioners, to gain a deeper understanding of the difficulties and limitations associated with common approaches and algorithms. We describe four families of problems for which some of the commonly used existing algorithms fail or suffer significant difficulty. We illustrate the failures through practical experiments, and provide theoretical insights explaining their source, and how they might be remedied.

Answer: Failures of Deep Learning

Question: Data can be acquired, shared, and processed by an increasingly larger number of entities, in particular people. The distributed nature of this phenomenon has contributed to the development of many crowdsourcing projects. This scenario is prevalent in most forms of expert/non-expert group opinion and rating tasks (including many forms of internet or on-line user behavior), where a key element is the aggregation of observations-opinions from multiple sources.

Answer:
Evaluating Crowdsourcing Participants in the Absence of Ground-Truth