Joint Learning of Correlated Sequence Labelling Tasks Using Bidirectional Recurrent Neural Networks

The stream of words produced by Automatic Speech Recognition (ASR) systems is typically devoid of punctuations and formatting. Most natural language processing applications expect segmented and well-formatted texts as input, which is not available in ASR output. This paper proposes a novel technique of jointly modeling multiple correlated tasks such as punctuation and capitalization using bidirectional recurrent neural networks, which leads to improved performance for each of these tasks. This method could be extended for joint modeling of any other correlated sequence labeling tasks.Comment: Accepted in Interspeech 201

Knowledge formalization in experience feedback processes : an ontology-based approach

Because of the current trend of integration and interoperability of industrial systems, their size and complexity continue to grow making it more difficult to analyze, to understand and to solve the problems that happen in their organizations. Continuous improvement methodologies are powerful tools in order to understand and to solve problems, to control the effects of changes and finally to capitalize knowledge about changes and improvements. These tools involve suitably represent knowledge relating to the concerned system. Consequently, knowledge management (KM) is an increasingly important source of competitive advantage for organizations. Particularly, the capitalization and sharing of knowledge resulting from experience feedback are elements which play an essential role in the continuous improvement of industrial activities. In this paper, the contribution deals with semantic interoperability and relates to the structuring and the formalization of an experience feedback (EF) process aiming at transforming information or understanding gained by experience into explicit knowledge. The reuse of such knowledge has proved to have significant impact on achieving themissions of companies. However, the means of describing the knowledge objects of an experience generally remain informal. Based on an experience feedback process model and conceptual graphs, this paper takes domain ontology as a framework for the clarification of explicit knowledge and know-how, the aim of which is to get lessons learned descriptions that are significant, correct and applicable

Text Injection for Capitalization and Turn-Taking Prediction in Speech Models

Text injection for automatic speech recognition (ASR), wherein unpaired text-only data is used to supplement paired audio-text data, has shown promising improvements for word error rate. This study examines the use of text injection for auxiliary tasks, which are the non-ASR tasks often performed by an E2E model. In this work, we use joint end-to-end and internal language model training (JEIT) as our text injection algorithm to train an ASR model which performs two auxiliary tasks. The first is capitalization, which is a de-normalization task. The second is turn-taking prediction, which attempts to identify whether a user has completed their conversation turn in a digital assistant interaction. We show results demonstrating that our text injection method boosts capitalization performance for long-tail data, and improves turn-taking detection recall

Lessons Learned about Change Capital in the Arts: Reflections on a four-year evaluation of Nonprofit Finance Fund's Leading for the Future initiative

This report takes stock of a four-year evaluation of Leading for the Future: Innovative Support for Artistic Excellence (LFF), an experimental $15 million funding initiative administered by Nonprofit Finance Fund (NFF) with support from the Doris Duke Charitable Foundation (DDCF). The purpose of this analysis is to reflect critically on what was learned from the initiative for the benefit of funders, individual philanthropists and others with an interest in the theory and practice of capitalization as applied to nonprofit arts organizations.The LFF initiative was unique in its exclusive focus on change capital – substantial, flexible, multi-year capital intended to transform how an organization operates and delivers its programs, with the long-term goal of increasing reliable revenue, net of costs. By definition, change capital aims to strengthen an organization's financial position.Ten performing arts organizations received$1 million in change capital, drawn down according to individual plans for change, and an additional $75,000 in planning funds. Exit grants of up to$225,000 were awarded to organizations that made the most progress on their change efforts, for the purpose of advancing ongoing change efforts or seeding new plans.1 The 10 grantees invested LFF change capital in a wide variety of "business model transformations" ranging from building technologies with the potential to attract new donors and audiences, to experimenting with different models for touring, to investing in marketing and development capacities.NFF has previously published a series of working papers, case studies and video highlights from the LFF initiative, exploring the concepts of capital and financial reporting for capital, and documenting the 10 grantees' experiences.2 We will avoid citing the accomplishments and challenges of specific grantees in this report, and focus instead on program level issues and ideas that might be helpful to future investors of change capital. Indeed, the LFF initiative has played out against the backdrop of a national dialogue about capitalization in the nonprofit arts sector, both learning from, and contributing to, a good deal of productive thinking about capital.While the LFF initiative involved large grants, much was learned that might be of value to funders with more modest resources who are interested in exploring the role of capital in the artistic and financial health of the sector