This paper presents experimental results to reduce the parameter space for word alignment algorithm. We use IBM Model 4 as a baseline. We applied a word lemmatizer program and a term extraction algorithm to preprocess a training corpus to reduce the model parameter space. We obtained an improvement in the alignment error rate by the additional components.
Available from the https://www.cs.unt.edu/~rada/wpt/NAACL/HLT Workshop. Building and Using Parallel Texts: Data Driven Machine Translation and Beyond website.
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Reducing Parameter Space for Word Alignment
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| Hervé Déjean, Eric Gaussier, Cyril Goutte, Kenji Yamada |
| https://www.cs.unt.edu/~rada/wpt/NAACL/HLT Workshop Building and Using Parallel Texts: Data Driven Machine Translation and Beyond, Edmonton, Canada, May 31, 2003. |
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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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