44,581 research outputs found

    Advancing Dispute Resolution by Unpacking the Sources of Conflict: Toward an Integrated Framework

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    Organizational leaders, public policy makers, dispute resolution professionals, and scholars have developed diverse methods for resolving workplace conflict. But there is inadequate recognition that the effectiveness of a dispute resolution method depends on its fit with the source of a particular conflict. Consequently, it is essential to better understand where conflict comes from and how this affects dispute resolution. To these ends, this paper uniquely integrates scholarship from multiple disciplines to develop a multi-dimensional framework on the sources of conflict. This provides an important foundation for theorizing and identifying effective dispute resolution methods, which are more important than ever as the changing world of work raises new issues, conflicts, and institutions

    Educational Leadership Coaching as Professional Development

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    As the burden of school leadership continues to increase in complexity, the need for reflective, collaborative leadership surges in tandem. The collaborative approach of educational leadership coaching develops school leaders and teacher leaders into meta-cognitive, reflective practitioners. Shoho, Barnett, and Martinez (2012) posited, Many school systems are embracing coaching as a way to influence and enhance leaders\u27 skill development, cognitive abilities, and emotional intelligence (p. 165). These skilled educational leaders can then seek solutions that allow for the complexity of the school systems while generating positive student outcomes, relational trust, and increased teacher efficacy

    Word-to-Word Models of Translational Equivalence

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    Parallel texts (bitexts) have properties that distinguish them from other kinds of parallel data. First, most words translate to only one other word. Second, bitext correspondence is noisy. This article presents methods for biasing statistical translation models to reflect these properties. Analysis of the expected behavior of these biases in the presence of sparse data predicts that they will result in more accurate models. The prediction is confirmed by evaluation with respect to a gold standard -- translation models that are biased in this fashion are significantly more accurate than a baseline knowledge-poor model. This article also shows how a statistical translation model can take advantage of various kinds of pre-existing knowledge that might be available about particular language pairs. Even the simplest kinds of language-specific knowledge, such as the distinction between content words and function words, is shown to reliably boost translation model performance on some tasks. Statistical models that are informed by pre-existing knowledge about the model domain combine the best of both the rationalist and empiricist traditions

    Neural Networks for Information Retrieval

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    Machine learning plays a role in many aspects of modern IR systems, and deep learning is applied in all of them. The fast pace of modern-day research has given rise to many different approaches for many different IR problems. The amount of information available can be overwhelming both for junior students and for experienced researchers looking for new research topics and directions. Additionally, it is interesting to see what key insights into IR problems the new technologies are able to give us. The aim of this full-day tutorial is to give a clear overview of current tried-and-trusted neural methods in IR and how they benefit IR research. It covers key architectures, as well as the most promising future directions.Comment: Overview of full-day tutorial at SIGIR 201

    Analyzing and Interpreting Neural Networks for NLP: A Report on the First BlackboxNLP Workshop

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    The EMNLP 2018 workshop BlackboxNLP was dedicated to resources and techniques specifically developed for analyzing and understanding the inner-workings and representations acquired by neural models of language. Approaches included: systematic manipulation of input to neural networks and investigating the impact on their performance, testing whether interpretable knowledge can be decoded from intermediate representations acquired by neural networks, proposing modifications to neural network architectures to make their knowledge state or generated output more explainable, and examining the performance of networks on simplified or formal languages. Here we review a number of representative studies in each category
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