Compositional models provide an elegant formalism for representing the visual appearance of highly variable objects. While such models are appealing from a theoretical point of view, it has been difficult to demonstrate that they lead to performance advantages on challenging datasets. Here we develop a grammar model for person detection and show that it outperforms previous high-performance systems on the PASCAL benchmark. Our model represents people using a hierarchy of deformable parts, variable structure and an explicit model of occlusion for partially visible objects. To train the model, we introduce a new discriminative framework for learning structured prediction models from weakly-labeled data. 1

This paper seeks to close the gap between training algorithms used in statistical machine translation and machine learning, specifically the framework of empirical risk minimization. We review well-known algorithms, arguing that they do not optimize the loss functions they are assumed to optimize when applied to machine translation. Instead, most have implicit connections to particular forms of ramp loss. We propose to minimize ramp loss directly and present a training algorithm that is easy to implement and that performs comparably to others. Most notably, our structured ramp loss minimization algorithm, RAMPION, is less sensitive to initialization and random seeds than standard approaches. 1

We study a problem with pairwise ranking optimization (PRO): that it tends to yield too short translations. We find that this is partially due to the inadequate smoothing in PRO’s BLEU+1, which boosts the precision component of BLEU but leaves the brevity penalty unchanged, thus destroying the balance between the two, compared to BLEU. It is also partially due to PRO optimizing for a sentence-level score without a global view on the overall length, which introducing a bias towards short translations; we show that letting PRO optimize a corpus-level BLEU yields a perfect length. Finally, we find some residual bias due to the interaction of PRO with BLEU+1: such a bias does not exist for a version of MIRA with sentence-level BLEU+1. We propose several ways to fix the length problem of PRO, including smoothing the brevity penalty, scaling the effective reference length, grounding the precision component, and unclipping the brevity penalty, which yield sizable improvements in test BLEU on two Arabic-English datasets: IWSLT (+0.65) and NIST (+0.37).