We give an overview of a MT research project jointly undertaken by Xerox PARC and XRCE Grenoble. The project builds on insights and resources in large-scale development of parallel LFG grammars. The research approach towards translation focuses on innovative computational technologies which lead to a flexible translation architecture. Efficient processing of ‘packed’ ambiguities not only enables ambiguity preserving transfer. It is at the heart of a flexible architectural design, open for various extensions which take the right decisions at the right time.
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From Parallel Grammar Development towards Machine Translation
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| Anette Frank |
| Proceedings of MT Summit VII. 'MT in the Great Translation Era', September 13-17, Kent Ridge Digital Labs, Singapore, pp. 134-142 (1999) |
VISION
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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