http://pub.hal3.name#daume07easyadapt FrustratinglyEasyDomainAdaptation HalDaum´eIII SchoolofComputing UniversityofUtah SaltLakeCity,Utah84112 me@hal3.name Abstract Wedescribeanapproachtodomainadapta- tionthatisappropriateexactlyinthecase whenonehasenough“target”datatodo ...
Daume, III, H. (2007). Frustratingly easy domain adaptation. In Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics, Prague, Czech Republic.Hal Daume´ III. Frustratingly easy domain adaptation. Association of Computational Linguis- tics, pages 256-263, 2007....
We describe an approach to domain adaptation that is appropriate exactly in the case when one has enough ``target'' data to do slightly better than just using only ``source'' data. Our approach is incredibly simple, easy to implement as a preprocessing step (10 lines of Perl!) and outper...
Return of frustratingly easy domain adaptationFrustratingly Hard Domain Adaptation for Dependency ParsingFrustratingly Short Attention Spans in Neural Language ModelingFrustratingly Easy Cross-Lingual Transfer for Transition-Based Dependency ParsingCritical infrastructure asset identification: seemingly simple but ...
Return of frustratingly easy domain adaptation Frustratingly Hard Domain Adaptation for Dependency Parsing Frustratingly Short Attention Spans in Neural Language Modeling Frustratingly Easy Cross-Lingual Transfer for Transition-Based Dependency Parsing Critical infrastructure asset identification: seemingly simple but...
Sarikaya, "Frustratingly easy neural domain adaptation," in COLING, 2016, pp. 387-396.Young-Bum Kim, Karl Stratos, and Ruhi Sarikaya, "Frustratingly easy neural domain adaptation," in Pro- ceedings of COLING 2016, the 26th International Con- ference on Computational Linguistics: Technical Pa-...
The general consensus in the literature is that this issue, known as domain adaptation and/or dataset bias, is due to a distribution mismatch between data collections. Methods addressing it go from max-margin classifiers to learning how to modify the features and obtain a more robust ...
Saenko. Return of frustratingly easy domain adaptation. CoRR, arXiv:1511.05547, 2015.Baochen Sun, Jiashi Feng, and Kate Saenko. Return of frustratingly ... B Sun,J Feng,K Saenko - Thirtieth Aaai Conference on Artificial Intelligence 被引量: 247发表: 2015年 Return of Frustratingly Easy Domain...
Average F-score with and without frustratingly easy domain adaptation (FEDA).Stephen WuTimothy MillerJames MasanzMatt CoarrScott HalgrimDavid CarrellCheryl Clark
Such domain shifts, common in practical scenarios, severely damage the performance of conventional machine learning methods. Supervised domain adaptation methods have been proposed for the case when the target data have labels, including some that perform very well despite being "frustratingly easy"...