We present novel models for domain adaptation based on the neural network joint model (NNJM). Our models maximize the cross entropy by regularizing the loss function with respect to in-domain model. Domain adaptation is carried out by assigning higher weight to out-domain sequences that are similar to the in-domain data. In our alternative model we take a more restrictive approach by additionally penalizing sequences similar to the outdomain data. Our models achieve better perplexities than the baseline NNJM models and give improvements of up to 0.5 and 0.6 BLEU points in Arabic-to-English and English-to-German language pairs, on a standard task of translating TED talks.

@inproceedings{joty-et-al-emnlp-15-1, abstract = {We present novel models for domain adaptation based on the neural network joint model (NNJM). Our models maximize the cross entropy by regularizing the loss function with respect to in-domain model. Domain adaptation is carried out by assigning higher weight to out-domain sequences that are similar to the in-domain data. In our alternative model we take a more restrictive approach by additionally penalizing sequences similar to the outdomain data. Our models achieve better perplexities than the baseline NNJM models and give improvements of up to 0.5 and 0.6 BLEU points in Arabic-to-English and English-to-German language pairs, on a standard task of translating TED talks.}, address = {Lisbon, Portugal}, author = {Joty, Shafiq and Sajjad, Hassan and Durrani, Nadir and Al-Mannai, Kamla and Abdelali, Ahmed and Vogel, Stephan}, booktitle = {Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing}, pages = {1259--1270}, publisher = {ACL}, series = {EMNLP'15}, title = {How to Avoid Unwanted Pregnancies: Domain Adaptation using Neural Network Models}, url = {http://aclweb.org/anthology/D15-1147}, year = {2015} }