From Compute to Data: Across-the-Stack System Design for Intelligent Applications

Intelligent applications such as Apple Siri, Google Assistant and Amazon Alexa have gained tremendous popularity in recent years. With human-like understanding capabilities and natural language interface, this class of applications is quickly becoming people’s preferred way of interacting with their mobile, wearable and smart home devices. There have been considerable advancement in machine learning research that aim to further enhance the understanding capability of intelligent applications, however there exist significant roadblocks in applying state-of-the-art algorithms and techniques to a real-world use case. First, as machine learning algorithms becomes more sophisticated, it imposes higher computation requirements for the underlying software and hardware system to process intelligent application request efficiently. Second, state-of-the-art algorithms and techniques is not guaranteed to provide the same level of prediction and classification accuracy when applied to tasks required in real-world intelligent applications, which are often different and more complex than what are studied in a research environment. This dissertation addresses these roadblocks by investigating the key challenges across multiple components in an intelligent application system. Specifically, we identify the key compute and data challenges and presents system design and techniques. To improve the computational performance of the hardware and software system, we challenge the status-quo approach of cloud-only intelligent application processing and propose computation partitioning strategies that effectively leverage both the cycles in the cloud and on the mobile device to achieve low latency, low energy consumption and high datacenter throughput. We characterize and taxonomize state-of-the- art deep learning based natural language processing (NLP) applications to identify the algorithmic design elements and computational patterns that render conventional GPU acceleration techniques ineffective on this class of applications. Leveraging their unique characteristics, we design and implement a novel fine-grain cross-input batching techniques for providing GPU acceleration to a number of state-of-the-art NLP applications. For the data component, large scale and effective training data, in addition to algorithm, is necessary to achieve high prediction accuracy. We investigate the challenge of effective large-scale training data collection via crowdsourcing. We propose novel metrics to evaluate the quality of training data for building real-word intelligent application systems. We leverage this methodology to study the trade-off of multiple crowdsourcing methods and provide recommendations on best training data crowdsourcing practices.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/145886/1/ypkang_1.pd

A Survey of Current Datasets for Vision and Language Research

Integrating vision and language has long been a dream in work on artificial intelligence (AI). In the past two years, we have witnessed an explosion of work that brings together vision and language from images to videos and beyond. The available corpora have played a crucial role in advancing this area of research. In this paper, we propose a set of quality metrics for evaluating and analyzing the vision & language datasets and categorize them accordingly. Our analyses show that the most recent datasets have been using more complex language and more abstract concepts, however, there are different strengths and weaknesses in each.Comment: To appear in EMNLP 2015, short proceedings. Dataset analysis and discussion expanded, including an initial examination into reporting bias for one of them. F.F. and N.M. contributed equally to this wor

Deep Learning-Based Knowledge Injection for Metaphor Detection: A Comprehensive Review

The history of metaphor research also marks the evolution of knowledge infusion research. With the continued advancement of deep learning techniques in recent years, the natural language processing community has shown great interest in applying knowledge to successful results in metaphor recognition tasks. Although there has been a gradual increase in the number of approaches involving knowledge injection in the field of metaphor recognition, there is a lack of a complete review article on knowledge injection based approaches. Therefore, the goal of this paper is to provide a comprehensive review of research advances in the application of deep learning for knowledge injection in metaphor recognition tasks. In this paper, we systematically summarize and generalize the mainstream knowledge and knowledge injection principles, as well as review the datasets, evaluation metrics, and benchmark models used in metaphor recognition tasks. Finally, we explore the current issues facing knowledge injection methods and provide an outlook on future research directions.Comment: 15 page