Scaling Up Crowd-Sourcing to Very Large Datasets: A Case for Active Learning

Active Learning for Crowd-Sourced Databases

Crowd-sourcing has become a popular means of acquiring labeled data for many tasks where humans are more accurate than computers, such as image tagging, entity resolution, or sentiment analysis. However, due to the time and cost of human labor, solutions that solely rely on crowd-sourcing are often limited to small datasets (i.e., a few thousand items). This paper proposes algorithms for integr...

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...

Active Learning with Crowd-Sourcing Semester Project Report

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...

Probabilistic Zero-shot Classification with Semantic Rankings

In this paper we propose a non-metric rankingbased representation of semantic similarity that allows natural aggregation of semantic information from multiple heterogeneous sources. We apply the ranking-based representation to zeroshot learning problems, and present deterministic and probabilistic zero-shot classifiers which can be built from pre-trained classifiers without retraining. We demon...