Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

How to Do Things With Words

Amazon’s Mechanical Turk is an online labor market where requesters post jobs and workers choose which jobs to do for pay. The central purpose of this paper is to demonstrate how to use this website for conducting behavioral research and lower the barrier to entry for researchers who could beneﬁt from this platform. We describe general techniques that apply to a variety of types of research and experiments across disciplines. We begin by discussing some of the advantages of doing experiments on Mechanical Turk, such as easy access to a large, stable, and diverse subject pool, the low cost of doing experiments and faster iteration between developing theory and executing experiments. We will discuss how the behavior of workers compares to experts and to laboratory subjects. Then, we illustrate the mechanics of putting a task on Mechanical Turk including recruiting subjects, executing the task, and reviewing the work that was submitted. We also provide solutions to common problems that a researcher might face when executing their research on this platform including techniques for conducting synchronous experiments, methods to ensure high quality work, how to keep data private, and how to maintain code security.

https://papers.ssrn.com/sol3/Delivery.cfm/SSRN_ID1798687_code1554670.pdf?abstractid=1691163&mirid=1&type=2

Conducting Behavioral Research on Amazon's Mechanical Turk

Amazon’s Mechanical Turk is an online labor market where requesters post jobs and workers choose which jobs to do for pay. The central purpose of this article is to demonstrate how to use this Web site for conducting behavioral research and to lower the barrier to entry for researchers who could benefit from this platform. We describe general techniques that apply to a variety of types of research and experiments across disciplines. We begin by discussing some of the advantages of doing experiments on Mechanical Turk, such as easy access to a large, stable, and diverse subject pool, the low cost of doing experiments, and faster iteration between developing theory and executing experiments. While other methods of conducting behavioral research may be comparable to or even better than Mechanical Turk on one or more of the axes outlined above, we will show that when taken as a whole Mechanical Turk can be a useful tool for many researchers. We will discuss how the behavior of workers compares with that of experts and laboratory subjects. Then we will illustrate the mechanics of putting a task on Mechanical Turk, including recruiting subjects, executing the task, and reviewing the work that was submitted. We also provide solutions to common problems that a researcher might face when executing their research on this platform, including techniques for conducting synchronous experiments, methods for ensuring high-quality work, how to keep data private, and how to maintain code security.

/pdf/conducting-behavioral-research-on-amazon-s-mechanical-turk-1xjwv92e5j.pdf

Conducting behavioral research on Amazon's Mechanical Turk.

人教版高中英语新课程教材中,语言运用（Using Language）是每个单元必不可少的部分,提供了围绕单元中心话题的听、说、读、写的综合性练习,是单元中心话题的延续和升华.如何设计Using Language部分的教学,使自己的教学模式既不落俗套,又能真正体现新课程标准所倡导的教学理念,正是广大一线英语教师一直努力探索的问题.

打磨Using Language,倡导新理念

With robotics rapidly advancing, more effective human-robot interaction is increasingly needed to realize the full potential of robots for society. While spoken language must be part of the solution, our ability to provide spoken language interaction capabilities is still very limited. The National Science Foundation accordingly convened a workshop, bringing together speech, language, and robotics researchers to discuss what needs to be done. The result is this report, in which we identify key scientific and engineering advances needed. 
Our recommendations broadly relate to eight general themes. First, meeting human needs requires addressing new challenges in speech technology and user experience design. Second, this requires better models of the social and interactive aspects of language use. Third, for robustness, robots need higher-bandwidth communication with users and better handling of uncertainty, including simultaneous consideration of multiple hypotheses and goals. Fourth, more powerful adaptation methods are needed, to enable robots to communicate in new environments, for new tasks, and with diverse user populations, without extensive re-engineering or the collection of massive training data. Fifth, since robots are embodied, speech should function together with other communication modalities, such as gaze, gesture, posture, and motion. Sixth, since robots operate in complex environments, speech components need access to rich yet efficient representations of what the robot knows about objects, locations, noise sources, the user, and other humans. Seventh, since robots operate in real time, their speech and language processing components must also. Eighth, in addition to more research, we need more work on infrastructure and resources, including shareable software modules and internal interfaces, inexpensive hardware, baseline systems, and diverse corpora.

Spoken Language Interaction with Robots: Research Issues and Recommendations, Report from the NSF Future Directions Workshop.

TeamTalk is a dialog framework that supports multiparticipant spoken interaction between humans and robots in a task-oriented setting that requires cooperation and coordination among team members. We describe two new features to the system, the ability for robots to accept and remember location labels and the ability to learn action sequences. These capabilities were made possible by incorporating an ontology and an instruction understanding component into the system.

Instruction Taking in the TeamTalk System

Situated dialogue is prominent in the robot navigation task, where a human gives route instructions (i.e., a sequence of navigation commands) to an agent. We propose an approach for situated dialogue agents whereby they use strategies such as asking questions to repair or recover from unclear instructions, namely those that an agent misunderstands or considers ambiguous. Most immediately in this work we study examples from existing human-human dialogue corpora and relate them to our proposed approach.

Towards Overcoming Miscommunication in Situated Dialogue by Asking Questions

Robots can use information from their surroundings to improve spoken language communication with people. Even when speech recognition is correct, robots face challenges when interpreting human instructions. These situated grounding problems include referential ambiguities and impossible-to-execute instructions. We present an approach to resolving situated grounding problems through spoken dialogue recovery strategies that robots can invoke to repair these problems. We describe a method for evaluating these strategies in human-robot navigation scenarios.

Towards evaluating recovery strategies for situated grounding problems in human-robot dialogue

We describe the creation of a new domain for the Methodius Natural Language Generation System, and an evaluation of Methodius' parameterized comparison generation algorithm. The new domain was based around music and performers, and texts about the domain were generated using Methodius. Our evaluation showed that test subjects learned more from texts that contained comparisons than from those that did not. We also established that the comparison generation algorithm could generalize to the music domain.

/pdf/creation-of-a-new-domain-and-evaluation-of-comparison-47f6qi2gip.pdf

Matthew Marge

Papers

Spoken Language Interaction with Robots: Research Issues and Recommendations, Report from the NSF Future Directions Workshop.

Instruction Taking in the TeamTalk System

Towards Overcoming Miscommunication in Situated Dialogue by Asking Questions

Towards evaluating recovery strategies for situated grounding problems in human-robot dialogue

Creation of a new domain and evaluation of comparison generation in a natural language generation system