We describe experiments on discriminating English to French phrase-based translations through the use of syntactic “coupling” features. Using a robust rule-based dependency parser, we parse both the English source and the French translation candidates from the nbest list returned by our phrase-based system; we compute for each candidate a number of coupling features, that is, values that depend on the amount of alignment between edges in the source and target structures, and discriminatively train the weights of these coupling features. We compare different feature combinations. Although the improvements in terms of automatic measures such as Bleu and Nist are inconclusive, an initial human assessment of the results appears to show certain qualitative improvements.
Experiments in Discriminating Phrase-based Translations on the Basis of Syntactic Coupling Features
| Vassilina Nikoulina, Marc Dymetman |
| 2nd Workshop on Syntax and Structure in Statistical Translation (SSST-2), ACL 2008, Colombus, Ohio, USA, June 19, 2008 <BR> Full paper available on ACL Website |
The research we conduct on expressive visual representations is applicable to visual search, object detection, image classification and the automatic extraction of 3D human poses and shapes that can be used for human behavior understanding and prediction, human-robot interaction or even avatar animation. We also extract 3D information from images that can be used for intelligent robot navigation, augmented reality and the 3D reconstruction of objects, buildings or even entire cities.
Our work covers the spectrum from unsupervised to supervised approaches, and from very deep architectures to very compact ones. We’re excited about the promise of big data to bring big performance gains to our algorithms but also passionate about the challenge of working in data-scarce and low-power scenarios.
Furthermore, we believe that a modern computer vision system needs to be able to continuously adapt itself to its environment and to improve itself via lifelong learning. Our driving goal is to use our research to deliver embodied intelligence to our users in robotics, autonomous driving, via phone cameras and any other visual means to reach people wherever they may be.
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