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ACL, pp.220-224, 2010."},{"key":"5","doi-asserted-by":"crossref","unstructured":"[5] R. Masumura, S. Hahm, and A. Ito, \u201cTraining a language model using webdata for large vocabulary Japanese spontaneous speech recognition,\u201d Proc. INTERSPEECH, pp.1465-1468, 2011. 10.21437\/interspeech.2011-258","DOI":"10.21437\/Interspeech.2011-258"},{"key":"6","doi-asserted-by":"crossref","unstructured":"[6] Z. T\u00fcske, K. Irie, R. Schl\u00fcter, and H. Ney, \u201cInvestigation on log-linear interpolation of multi-domain neural network language model,\u201d Proc. ICASSP, pp.6005-6009, 2016. 10.1109\/icassp.2016.7472830","DOI":"10.1109\/ICASSP.2016.7472830"},{"key":"7","doi-asserted-by":"crossref","unstructured":"[7] S. Deena, M. Hasan, M. Doulaty, O. Saz, and T. Hain, \u201cCombining feature and model-based adaptation of RNNLMs for multi-genre broadcast speech recognition,\u201d Proc. INTERSPEECH, pp.2343-2347, 2016. 10.21437\/interspeech.2016-480","DOI":"10.21437\/Interspeech.2016-480"},{"key":"8","unstructured":"[8] H. Daum\u00e9 III, \u201cFrustratingly easy domain adaptation,\u201d Proc. ACL, pp.256-263, 2007."},{"key":"9","doi-asserted-by":"crossref","unstructured":"[9] J. Park, X. Liu, M.J.F. Gales, and P.C. Woodland, \u201cImproved neural network based language modelling and adaptation,\u201d Proc. INTERSPEECH, pp.1041-1044, 2010. 10.21437\/interspeech.2010-342","DOI":"10.21437\/Interspeech.2010-342"},{"key":"10","unstructured":"[10] K. Bousmalis, G. Trigeorgis, N. Silberman, D. Krishnan, and D. Erhan, \u201cDomain separation networks,\u201d Proc. NIPS, pp.343-351, 2016."},{"key":"11","unstructured":"[11] K. Young-Bum, S. Karl, and K. Dongchan, \u201cDomain attention with an ensemble of experts,\u201d Proc. ACL, pp.643-653, 2017. 10.18653\/v1\/p17-1060"},{"key":"12","doi-asserted-by":"crossref","unstructured":"[12] K. Irie, S. Kumar, M. Nirschl, and H. Liao, \u201cRADMM: recurrent adaptive mixture model with applications to domain robust language modeling,\u201d Proc. ICASSP, pp.6079-6083, 2018. 10.1109\/icassp.2018.8461628","DOI":"10.1109\/ICASSP.2018.8461628"},{"key":"13","unstructured":"[13] J. Zhang, X. Wu, A. Way, and Q. Liu, \u201cFast gated neural domain adaptation: Language model as a case study,\u201d Proc. COLING, pp.1386-1397, 2016."},{"key":"14","unstructured":"[14] E. Garmash and C. Monz, \u201cEnsemble learning for multi-source neural machine translation,\u201d Proc. COLING, pp.1409-1418, 2016."},{"key":"15","doi-asserted-by":"crossref","unstructured":"[15] S. Li and C. Zong, \u201cMulti-domain sentiment classification,\u201d Proc. ACL: HLT, Short Papers, pp.257-260, 2008. 10.3115\/1557690.1557765","DOI":"10.3115\/1557690.1557765"},{"key":"16","doi-asserted-by":"crossref","unstructured":"[16] J. Guo, D. Shah, and R. Barzilay, \u201cMulti-source domain adaptation with mixture of experts,\u201d Proc. EMNLP, pp.4694-4703, 2018. 10.18653\/v1\/d18-1498","DOI":"10.18653\/v1\/D18-1498"},{"key":"17","doi-asserted-by":"crossref","unstructured":"[17] R.A. Jacobs, M.I. Jordan, S.J. Nowlan, and G.E. Hinton, \u201cAdaptive mixtures of local experts,\u201d Neural Comput, vol.3, no.1, pp.79-87, 1991. 10.1162\/neco.1991.3.1.79","DOI":"10.1162\/neco.1991.3.1.79"},{"key":"18","unstructured":"[18] G. Blanchard, G. Lee, and C. Scott, \u201cGeneralizing from several related classification tasks to a new unlabeled sample,\u201d Proc. NIPS, pp.2178-2186, 2011."},{"key":"19","unstructured":"[19] K. Muandet, D. Balduzzi, and B. Sch\u00f6lkopf, \u201cDomain generalization via invariant feature representation,\u201d Proc. ICML, pp.10-18, 2013."},{"key":"20","doi-asserted-by":"crossref","unstructured":"[20] C. Gan, T. Yang, and B. Gong, \u201cLearning attributes equals multi-source domain generalization,\u201d Proc. CVPR, pp.87-97, 2016. 10.1109\/cvpr.2016.17","DOI":"10.1109\/CVPR.2016.17"},{"key":"21","doi-asserted-by":"crossref","unstructured":"[21] M. Ghifary, W.B. Kleijn, M. Zhang, and D. Balduzzi, \u201cDomain generalization for object recognition with multi-task autoencoders,\u201d Proc. ICCV, pp.2551-2559, 2015. 10.1109\/iccv.2015.293","DOI":"10.1109\/ICCV.2015.293"},{"key":"22","doi-asserted-by":"crossref","unstructured":"[22] D. Li, Y. Yang, Y.-Z. Song, and T.M. Hospedales, \u201cDeeper, broader and artier domain generalization,\u201d Proc. ICCV, pp.5543-5551, 2017. 10.1109\/iccv.2017.591","DOI":"10.1109\/ICCV.2017.591"},{"key":"23","unstructured":"[23] Y. Ganin, E. Ustinova, H. Ajakan, P. Germain, H. Larochelle, F. Laviolette, M. Marchand, and V. Lempitsky, \u201cDomain-adversarial training of neural networks,\u201d Journal of Machine Learning Research, vol.17, pp.59:1-59:35, 2016. 10.1007\/978-3-319-58347-1_10"},{"key":"24","unstructured":"[24] S. Shankar, V. Piratla, S. Chakrabarti, S. Chaudhuri, P. Jyothi, and S. Sarawagi, \u201cGeneralizing across domains via cross-gradient training,\u201d CoRR, vol.abs\/1804.10745, 2018."},{"key":"25","doi-asserted-by":"publisher","unstructured":"[25] S. Greenbaum and G. Nelson, \u201cThe international corpus of English (ICE) project,\u201d World Englishes, vol.15, no.1, pp.3-15, 1996. 10.1111\/j.1467-971x.1996.tb00088.x","DOI":"10.1111\/j.1467-971X.1996.tb00088.x"},{"key":"26","unstructured":"[26] S. Tokui, K. Oono, S. Hido, and J. Clayton, \u201cChainer: a next-generation open source framework for deep learning,\u201d Proc. workshop on LearningSys in NIPS, pp.1-6, 2015."},{"key":"27","unstructured":"[27] D.P. Kingma and J. Ba, \u201cAdam: A method for stochastic optimization,\u201d arXiv preprint arXiv:1412.6980, 2014."},{"key":"28","unstructured":"[28] N. Ide and K. Suderman, \u201cThe American national corpus first release,\u201d Proc. LREC, pp.1681-1684, 2004."},{"key":"29","unstructured":"[29] G. 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