We estimate the parameters of a phrasebased statistical machine translation system from monolingual corpora instead of a bilingual parallel corpus. We extend existing research on bilingual lexicon induction to estimate both lexical and phrasal translation probabilities for MT-scale phrasetables. We propose a novel algorithm to estimate reordering probabilities from monolingual data. We report translation results for an end-to-end translation system using these monolingual features alone. Our method only requires monolingual corpora in source and target languages, a small bilingual dictionary, and a small bitext for tuning feature weights. In this paper, we examine an idealization where a phrase-table is given. We examine the degradation in translation performance when bilingually estimated translation probabilities are removed and show that 80%+ of the loss can be recovered with monolingually estimated features alone. We further show that our monolingual features add 1.5 BLEU points when combined with standard bilingually estimated phrase table features. 1

We describe a novel mechanism called Reservoir Counting for application in online Locality Sensitive Hashing. This technique allows for significant savings in the streaming setting, allowing for maintaining a larger number of signatures, or an increased level of approximation accuracy at a similar memory footprint. 1

Most NLP systems make predictions based solely on linguistic (textual or spoken) input. We show how to use visual information to make better linguistic predictions. We focus on selectional preference; specifically, determining the plausible noun arguments for particular verb predicates. For each argument noun, we extract visual features from corresponding images on the web. For each verb predicate, we train a classifier to select the visual features that are indicative of its preferred arguments. We show that for certain verbs, using visual information can significantly improve performance over a baseline. For the successful cases, visual information is useful even in the presence of cooccurrence information derived from webscale text. We assess a variety of training configurations, which vary over classes of visual features, methods of image acquisition, and numbers of images. 1