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Translated Learning Wenyuan Dai, Yuqiang Chen, Gui-Rong Xue, Qiang Yang, and Yong Yu. Translated Learning. In Proceedings of Twenty- Second Annual Conference.

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Presentation on theme: "Translated Learning Wenyuan Dai, Yuqiang Chen, Gui-Rong Xue, Qiang Yang, and Yong Yu. Translated Learning. In Proceedings of Twenty- Second Annual Conference."— Presentation transcript:

1 Translated Learning Wenyuan Dai, Yuqiang Chen, Gui-Rong Xue, Qiang Yang, and Yong Yu. Translated Learning. In Proceedings of Twenty- Second Annual Conference on Neural Information Processing Systems (NIPS 2008), December 8, 2008, Vancouver, British Columbia, Canada.

2 Definition Translated Learning – Learning across Different Feature Spaces

3 Applications Cross-language Classification – Rigutini et al., WI2005; Ling et al., WWW2008; … Text-aided Image Classification – We submitted two papers to AAAI2008 & ICML2008 respectively. Future work – Text to Music – Text to Video – Image to Video –…–…

4 Related Work Cross-language Classification – Rigutini et al., WI2005 English to Italian – Ling et al., WWW2008 English to Chinese Most cross-language classification approaches relies on machine translation. – Ad hoc – Machine translation is difficult in most scenarios. E.g. text-to-picture translation

5 Basic Idea Instance-level machine translation relies on understanding instances, at least basically. – Machine translation in NLP is an easy special case, since it is based on sentence understanding. Classification models are usually on the feature- level. Translating classification models is also on the feature-level. – could be much easier than instance-level translation

6 Human Learning Example Task: tyrannosaurus vs stegosaurus – htyrannosaurus: bipedal carnivore with a massive skull balanced by long, heavy tail. Its forelimbs were small and retained only two digits. – stegosaurus: quadruped ornithischian dinosaur of four long bony spikes on a flexible tail and two rows of upright triangular bony plates running along the back.

7 Model-level Translation Learning InputOutput Learning InputOutput Elephants are big mammals on earth... massive hoofed mammal of Africa... translating learning models

8 Naive Bayesian Approach Incorporating translator difficult to estimate

9 Risk Minimization Approach Loss function

10 Inference Assume there is no prior difference among all the classes

11 Model Estimation KL-divergence Negative of cosine Negative of PCC

12 Experimental Results

13

14 Outline Introduction Related Work Our Research Future Work

15 More applications – Cross-language classification Using dictionaries as translators – Text to music, video, … – Image to video Improving translator estimation – Integrating text classification and translator estimation into one optimization model

16 Questions


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