The Use of Unlabeled Data versus Labeled Data for Stopping Active Learning for Text Classification

Annotation of training data is the major bottleneck in the creation of text classification systems. Active learning is a commonly used technique to reduce the amount of training data one needs to label. A crucial aspect of active learning is determining when to stop labeling data. Three potential sources for informing when to stop active learning are an additional labeled set of data, an unlabeled set of data, and the training data that is labeled during the process of active learning. To date, no one has compared and contrasted the advantages and disadvantages of stopping methods based on these three information sources. We find that stopping methods that use unlabeled data are more effective than methods that use labeled data.Comment: 8 pages, 4 figures, 3 tables; published in Proceedings of the IEEE 13th International Conference on Semantic Computing (ICSC), Newport Beach, CA, USA, pages 287-294, January 201

Translation Model Size Reduction for Hierarchical Phrase-based Statistical Machine Translation

In this paper, we propose a novel method of reducing the size of translation model for hierarchical phrase-based machine translation systems. Previous approaches try to prune infrequent entries or unreliable entries based on statistics, but cause a problem of reducing the translation coverage. On the contrary, the proposed method try to prune only ineffective entries based on the estimation of the information redundancy encoded in phrase pairs and hierarchical rules, and thus preserve the search space of SMT decoders as much as possible. Experimental results on Chinese-to-English machine translation tasks show that our method is able to reduce almost the half size of the translation model with very tiny degradation of translation performance.