Previous work on paraphrase extraction and application has relied on either parallel datasets, or on distributional similarity metrics over large text corpora. Our approach combines these two orthogonal sources of information and directly integrates them into our paraphrasing system’s log-linear model. We compare different distributional similarity feature-sets and show significant improvements...

We propose a statistical sentence simplification system with log-linear models. In contrast to state-of-the-art methods that drive sentence simplification process by hand-written linguistic rules, our method used a margin-based discriminative learning algorithm operates on a feature set. The feature set is defined on statistics of surface form as well as syntactic and dependency structures of t...

We describe a generic framework for integrating various stochastic models of discourse coherence in a manner that takes advantage of their individual strengths. An integral part of this framework are algorithms for searching and training these stochastic coherence models. We evaluate the performance of our models and algorithms and show empirically that utilitytrained log-linear coherence model...

This paper proposes using n-gram posterior probabilities, which are estimated over translation hypotheses from multiple machine translation (MT) systems, to improve the performance of the system combination. Two ways using n-gram posteriors in confusion network decoding are presented. The first way is based on n-gram posterior language model per source sentence, and the second, called n-gram se...

The assessment of the influence of individual observations on the outcome of the analysis by perturbation has received a lot of attention for situations in which the observations are independent and identically distributed. However, no methods based on minor perturbations for carrying out such assessments are available in the context of multinomial models. A simultaneous perturbation scheme for...

In hierarchical phrase-based SMT systems, statistical models are integrated to guide the hierarchical rule selection for better translation performance. Previous work mainly focused on the selection of either the source side of a hierarchical rule or the target side of a hierarchical rule rather than considering both of them simultaneously. This paper presents a joint model to predict the selec...

We address the task of annotating images with semantic tuples. Solving this problem requires an algorithm able to deal with hundreds of classes for each argument of the tuple. In such contexts, data sparsity becomes a key challenge. We propose handling this sparsity by incorporating feature representations of both the inputs (images) and outputs (argument classes) into a factorized log-linear m...

This paper describes a joint model of word segmentation and phonological alternations, which takes unsegmented utterances as input and infers word segmentations and underlying phonological representations. The model is a Maximum Entropy or log-linear model, which can express a probabilistic version of Optimality Theory (OT; Prince and Smolensky (2004)), a standard phonological framework. The fe...

The position paper provides a brief summary of log-linear description logics and their applications. We compile a list of five requirements that we believe a probabilistic description logic should have to be useful in practice. We demonstrate the ways in which log-linear description logics answer to these requirements.