Learning motion primitives and annotative texts from crowd-sourcing

Active Learning and Crowd-Sourcing for Machine Translation

In recent years, corpus based approaches to machine translation have become predominant, with Statistical Machine Translation (SMT) being the most actively progressing area. Success of these approaches depends on the availability of parallel corpora. In this paper we propose Active Crowd Translation (ACT), a new paradigm where active learning and crowd-sourcing come together to enable automatic...

Crowd Sourcing and Prediction Markets

We introduce our work on the use of prediction markets as a tool for mining knowledge from within an enterprise (i.e., Nokia Corporation). Various companies – including Google, HP, and Yahoo – have experimented with internal prediction market services, testing its utility for tapping knowledge distributed and nascent within their organization. We are also exploring the use of this tool for capt...

Pronunciation learning for named-entities through crowd-sourcing

Obtaining good pronunciations for named-entities poses a challenge for automated speech recognition because namedentities are diverse in nature and origin, and new entities come up every day. In this paper, we investigate the feasibility of learning named-entity pronunciations using crowd-sourcing. By collecting audio samples from non-linguistic-expert speakers with Mechanical Turk and learning...

Machine-Learning for Spammer Detection in Crowd-Sourcing

Over a series of evaluation experiments conducted using naive judges recruited and managed via Amazon’s Mechanical Turk facility using a task from information retrieval (IR), we show that a SVM shows itself to have a very high accuracy when the machine-learner is trained and tested on a single task and that the method was portable from more complex tasks to simpler tasks, but not vice versa.

Active Learning with Crowd-Sourcing Semester Project Report