{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,28]],"date-time":"2025-06-28T06:44:53Z","timestamp":1751093093028,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":45,"publisher":"ACM","license":[{"start":{"date-parts":[[2019,10,14]],"date-time":"2019-10-14T00:00:00Z","timestamp":1571011200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["NSF CAREER 1651740"],"award-info":[{"award-number":["NSF CAREER 1651740"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2019,10,14]]},"DOI":"10.1145\/3340555.3353731","type":"proceedings-article","created":{"date-parts":[[2019,10,17]],"date-time":"2019-10-17T12:49:48Z","timestamp":1571316588000},"page":"174-184","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["Controlling for Confounders in Multimodal Emotion Classification via Adversarial Learning"],"prefix":"10.1145","author":[{"given":"Mimansa","family":"Jaiswal","sequence":"first","affiliation":[{"name":"University of Michigan, USA"}]},{"given":"Zakaria","family":"Aldeneh","sequence":"additional","affiliation":[{"name":"University of Michigan, USA"}]},{"given":"Emily","family":"Mower Provost","sequence":"additional","affiliation":[{"name":"University of Michigan, USA"}]}],"member":"320","published-online":{"date-parts":[[2019,10,14]]},"reference":[{"doi-asserted-by":"publisher","key":"e_1_3_2_1_1_1","DOI":"10.1109\/TASLP.2018.2867099"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_2_1","DOI":"10.1177\/0146167297234003"},{"key":"e_1_3_2_1_3_1","volume-title":"A theory of learning from different domains. Machine learning 79, 1-2","author":"Ben-David Shai","year":"2010","unstructured":"Shai Ben-David , John Blitzer , Koby Crammer , Alex Kulesza , Fernando Pereira , and Jennifer\u00a0Wortman Vaughan . 2010. A theory of learning from different domains. Machine learning 79, 1-2 ( 2010 ), 151\u2013175. Shai Ben-David, John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, and Jennifer\u00a0Wortman Vaughan. 2010. A theory of learning from different domains. Machine learning 79, 1-2 (2010), 151\u2013175."},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_4_1","DOI":"10.1111\/j.2517-6161.1995.tb02031.x"},{"doi-asserted-by":"crossref","unstructured":"Tony\u00a0W Buchanan Jacqueline\u00a0S Laures-Gore and Melissa\u00a0C Duff. 2014. Acute stress reduces speech fluency. Biological psychology 97(2014) 60\u201366.  Tony\u00a0W Buchanan Jacqueline\u00a0S Laures-Gore and Melissa\u00a0C Duff. 2014. Acute stress reduces speech fluency. Biological psychology 97(2014) 60\u201366.","key":"e_1_3_2_1_5_1","DOI":"10.1016\/j.biopsycho.2014.02.005"},{"key":"e_1_3_2_1_6_1","volume-title":"IEMOCAP: Interactive emotional dyadic motion capture database. Language resources and evaluation 42, 4","author":"Busso Carlos","year":"2008","unstructured":"Carlos Busso , Murtaza Bulut , Chi-Chun Lee , Abe Kazemzadeh , Emily Mower , Samuel Kim , Jeannette\u00a0 N Chang , Sungbok Lee , and Shrikanth\u00a0 S Narayanan . 2008 . IEMOCAP: Interactive emotional dyadic motion capture database. Language resources and evaluation 42, 4 (2008), 335. Carlos Busso, Murtaza Bulut, Chi-Chun Lee, Abe Kazemzadeh, Emily Mower, Samuel Kim, Jeannette\u00a0N Chang, Sungbok Lee, and Shrikanth\u00a0S Narayanan. 2008. IEMOCAP: Interactive emotional dyadic motion capture database. Language resources and evaluation 42, 4 (2008), 335."},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_7_1","DOI":"10.1109\/TAFFC.2016.2515617"},{"unstructured":"Fran\u00e7ois Chollet. 2015. keras. https:\/\/github.com\/fchollet\/keras.  Fran\u00e7ois Chollet. 2015. keras. https:\/\/github.com\/fchollet\/keras.","key":"e_1_3_2_1_8_1"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_9_1","DOI":"10.1016\/S0010-0277(02)00017-3"},{"doi-asserted-by":"crossref","unstructured":"Sheldon Cohen Tom Kamarck and Robin Mermelstein. 1983. A global measure of perceived stress. Journal of health and social behavior(1983) 385\u2013396.  Sheldon Cohen Tom Kamarck and Robin Mermelstein. 1983. A global measure of perceived stress. Journal of health and social behavior(1983) 385\u2013396.","key":"e_1_3_2_1_10_1","DOI":"10.2307\/2136404"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_11_1","DOI":"10.1177\/147470491100900406"},{"unstructured":"Emily\u00a0D. Duvall Alan\u00a0Stuart Robbins Thomas\u00a0R Graham and Scott Divett. 2014. Exploring Filler Words and Their Impact.  Emily\u00a0D. Duvall Alan\u00a0Stuart Robbins Thomas\u00a0R Graham and Scott Divett. 2014. Exploring Filler Words and Their Impact.","key":"e_1_3_2_1_12_1"},{"doi-asserted-by":"crossref","unstructured":"Yanai Elazar and Yoav Goldberg. 2018. Adversarial Removal of Demographic Attributes from Text Data. arXiv preprint arXiv:1808.06640(2018).  Yanai Elazar and Yoav Goldberg. 2018. Adversarial Removal of Demographic Attributes from Text Data. arXiv preprint arXiv:1808.06640(2018).","key":"e_1_3_2_1_13_1","DOI":"10.18653\/v1\/D18-1002"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_14_1","DOI":"10.1176\/jnp.2009.21.1.52"},{"unstructured":"Yaroslav Ganin and Victor Lempitsky. 2014. Unsupervised domain adaptation by backpropagation. arXiv preprint arXiv:1409.7495(2014).  Yaroslav Ganin and Victor Lempitsky. 2014. Unsupervised domain adaptation by backpropagation. arXiv preprint arXiv:1409.7495(2014).","key":"e_1_3_2_1_15_1"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_16_1","DOI":"10.5555\/2946645.2946704"},{"unstructured":"John Gideon Melvin\u00a0G McInnis and Emily Mower\u00a0Provost. 2019. Barking up the Right Tree: Improving Cross-Corpus Speech Emotion Recognition with Adversarial Discriminative Domain Generalization (ADDoG). arXiv preprint arXiv:1903.12094(2019).  John Gideon Melvin\u00a0G McInnis and Emily Mower\u00a0Provost. 2019. Barking up the Right Tree: Improving Cross-Corpus Speech Emotion Recognition with Adversarial Discriminative Domain Generalization (ADDoG). arXiv preprint arXiv:1903.12094(2019).","key":"e_1_3_2_1_17_1"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_18_1","DOI":"10.1109\/TIP.2017.2756450"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_19_1","DOI":"10.1109\/ICASSP.2019.8682793"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_20_1","DOI":"10.1007\/s10919-015-0209-5"},{"doi-asserted-by":"crossref","unstructured":"Jeffrey\u00a0H Kahn Renee\u00a0M Tobin Audra\u00a0E Massey and Jennifer\u00a0A Anderson. 2007. Measuring emotional expression with the Linguistic Inquiry and Word Count. The American journal of psychology(2007) 263\u2013286.  Jeffrey\u00a0H Kahn Renee\u00a0M Tobin Audra\u00a0E Massey and Jennifer\u00a0A Anderson. 2007. Measuring emotional expression with the Linguistic Inquiry and Word Count. The American journal of psychology(2007) 263\u2013286.","key":"e_1_3_2_1_21_1","DOI":"10.2307\/20445398"},{"doi-asserted-by":"crossref","unstructured":"Soheil Khorram Zakaria Aldeneh Dimitrios Dimitriadis Melvin McInnis and Emily\u00a0Mower Provost. 2017. Capturing long-term temporal dependencies with convolutional networks for continuous emotion recognition. arXiv preprint arXiv:1708.07050(2017).  Soheil Khorram Zakaria Aldeneh Dimitrios Dimitriadis Melvin McInnis and Emily\u00a0Mower Provost. 2017. Capturing long-term temporal dependencies with convolutional networks for continuous emotion recognition. arXiv preprint arXiv:1708.07050(2017).","key":"e_1_3_2_1_22_1","DOI":"10.21437\/Interspeech.2017-548"},{"doi-asserted-by":"crossref","unstructured":"Soheil Khorram Mimansa Jaiswal John Gideon Melvin McInnis and Emily\u00a0Mower Provost. 2018. The PRIORI Emotion Dataset: Linking Mood to Emotion Detected In-the-Wild. arXiv preprint arXiv:1806.10658(2018).  Soheil Khorram Mimansa Jaiswal John Gideon Melvin McInnis and Emily\u00a0Mower Provost. 2018. The PRIORI Emotion Dataset: Linking Mood to Emotion Detected In-the-Wild. arXiv preprint arXiv:1806.10658(2018).","key":"e_1_3_2_1_23_1","DOI":"10.21437\/Interspeech.2018-2355"},{"doi-asserted-by":"crossref","unstructured":"Yoon Kim. 2014. Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882(2014).  Yoon Kim. 2014. Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882(2014).","key":"e_1_3_2_1_24_1","DOI":"10.3115\/v1\/D14-1181"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_26_1","DOI":"10.1109\/ICASSP.2018.8462210"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_27_1","DOI":"10.5555\/3015812.3015840"},{"unstructured":"Francesco Locatello Stefan Bauer Mario Lucic Sylvain Gelly Bernhard Sch\u00f6lkopf and Olivier Bachem. 2018. Challenging common assumptions in the unsupervised learning of disentangled representations. arXiv preprint arXiv:1811.12359(2018).  Francesco Locatello Stefan Bauer Mario Lucic Sylvain Gelly Bernhard Sch\u00f6lkopf and Olivier Bachem. 2018. Challenging common assumptions in the unsupervised learning of disentangled representations. arXiv preprint arXiv:1811.12359(2018).","key":"e_1_3_2_1_28_1"},{"doi-asserted-by":"crossref","unstructured":"Karttikeya Mangalam and Tanaya Guha. 2017. Learning Spontaneity to Improve Emotion Recognition in Speech. arXiv preprint arXiv:1712.04753(2017).  Karttikeya Mangalam and Tanaya Guha. 2017. Learning Spontaneity to Improve Emotion Recognition in Speech. arXiv preprint arXiv:1712.04753(2017).","key":"e_1_3_2_1_29_1","DOI":"10.21437\/Interspeech.2018-1872"},{"doi-asserted-by":"crossref","unstructured":"Robert McHardy Heike Adel and Roman Klinger. 2019. Adversarial Training for Satire Detection: Controlling for Confounding Variables. arXiv preprint arXiv:1902.11145(2019).  Robert McHardy Heike Adel and Roman Klinger. 2019. Adversarial Training for Satire Detection: Controlling for Confounding Variables. arXiv preprint arXiv:1902.11145(2019).","key":"e_1_3_2_1_30_1","DOI":"10.18653\/v1\/N19-1069"},{"unstructured":"Robert McHardy Heike Adel and Roman Klinger. 2019. Adversarial Training for Satire Detection: Controlling for Confounding Variables. CoRR abs\/1902.11145(2019). arxiv:1902.11145http:\/\/arxiv.org\/abs\/1902.11145  Robert McHardy Heike Adel and Roman Klinger. 2019. Adversarial Training for Satire Detection: Controlling for Confounding Variables. CoRR abs\/1902.11145(2019). arxiv:1902.11145http:\/\/arxiv.org\/abs\/1902.11145","key":"e_1_3_2_1_31_1"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_32_1","DOI":"10.1073\/pnas.1707373114"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_33_1","DOI":"10.1109\/ICASSP.2018.8461932"},{"unstructured":"Tomas Mikolov Ilya Sutskever Kai Chen Greg\u00a0S Corrado and Jeff Dean. 2013. Distributed representations of words and phrases and their compositionality. In Advances in neural information processing systems. 3111\u20133119.  Tomas Mikolov Ilya Sutskever Kai Chen Greg\u00a0S Corrado and Jeff Dean. 2013. Distributed representations of words and phrases and their compositionality. In Advances in neural information processing systems. 3111\u20133119.","key":"e_1_3_2_1_34_1"},{"key":"e_1_3_2_1_35_1","volume-title":"Pruning convolutional neural networks for resource efficient transfer learning. arXiv preprint arXiv:1611.06440 3","author":"Molchanov Pavlo","year":"2016","unstructured":"Pavlo Molchanov , Stephen Tyree , Tero Karras , Timo Aila , and Jan Kautz . 2016. Pruning convolutional neural networks for resource efficient transfer learning. arXiv preprint arXiv:1611.06440 3 ( 2016 ). Pavlo Molchanov, Stephen Tyree, Tero Karras, Timo Aila, and Jan Kautz. 2016. Pruning convolutional neural networks for resource efficient transfer learning. arXiv preprint arXiv:1611.06440 3 (2016)."},{"unstructured":"John Moore Joel Pfeiffer Kai Wei Rishabh Iyer Denis Charles Ran Gilad-Bachrach Levi Boyles and Eren Manavoglu. 2018. Modeling and Simultaneously Removing Bias via Adversarial Neural Networks. arXiv preprint arXiv:1804.06909(2018).  John Moore Joel Pfeiffer Kai Wei Rishabh Iyer Denis Charles Ran Gilad-Bachrach Levi Boyles and Eren Manavoglu. 2018. Modeling and Simultaneously Removing Bias via Adversarial Neural Networks. arXiv preprint arXiv:1804.06909(2018).","key":"e_1_3_2_1_36_1"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_37_1","DOI":"10.1371\/journal.pone.0165022"},{"key":"e_1_3_2_1_38_1","first-page":"2001","article-title":"Linguistic inquiry and word count: LIWC 2001. Mahway","volume":"71","author":"Pennebaker W","year":"2001","unstructured":"James\u00a0 W Pennebaker , Martha\u00a0 E Francis , and Roger\u00a0 J Booth . 2001 . Linguistic inquiry and word count: LIWC 2001. Mahway : Lawrence Erlbaum Associates 71 , 2001 (2001), 2001 . James\u00a0W Pennebaker, Martha\u00a0E Francis, and Roger\u00a0J Booth. 2001. Linguistic inquiry and word count: LIWC 2001. Mahway: Lawrence Erlbaum Associates 71, 2001 (2001), 2001.","journal-title":"Lawrence Erlbaum Associates"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_39_1","DOI":"10.21437\/Interspeech.2012-131"},{"volume-title":"INTERSPEECH.","author":"Shinohara Yusuke","unstructured":"Yusuke Shinohara . 2016. Adversarial Multi-Task Learning of Deep Neural Networks for Robust Speech Recognition .. In INTERSPEECH. San Francisco, CA, USA , 2369\u20132372. Yusuke Shinohara. 2016. Adversarial Multi-Task Learning of Deep Neural Networks for Robust Speech Recognition.. In INTERSPEECH. San Francisco, CA, USA, 2369\u20132372.","key":"e_1_3_2_1_40_1"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_41_1","DOI":"10.1109\/ICASSP.1999.758342"},{"unstructured":"Adarsh Subbaswamy Peter Schulam and Suchi Saria. 2018. Learning predictive models that transport. arXiv preprint arXiv:1812.04597(2018).  Adarsh Subbaswamy Peter Schulam and Suchi Saria. 2018. Learning predictive models that transport. arXiv preprint arXiv:1812.04597(2018).","key":"e_1_3_2_1_42_1"},{"key":"e_1_3_2_1_43_1","volume-title":"COURSERA: Neural Networks for Machine Learning.","author":"Tieleman T.","year":"2012","unstructured":"T. Tieleman and G. Hinton . 2012 . Lecture 6.5\u2014RmsProp: Divide the gradient by a running average of its recent magnitude. COURSERA: Neural Networks for Machine Learning. T. Tieleman and G. Hinton. 2012. Lecture 6.5\u2014RmsProp: Divide the gradient by a running average of its recent magnitude. COURSERA: Neural Networks for Machine Learning."},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_44_1","DOI":"10.1109\/CVPR.2017.316"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_45_1","DOI":"10.1111\/apps.12065"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_46_1","DOI":"10.1145\/3278721.3278779"}],"event":{"acronym":"ICMI '19","name":"ICMI '19: INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION","location":"Suzhou China"},"container-title":["2019 International Conference on Multimodal Interaction"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3340555.3353731","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3340555.3353731","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3340555.3353731","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T23:13:28Z","timestamp":1750202008000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3340555.3353731"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,10,14]]},"references-count":45,"alternative-id":["10.1145\/3340555.3353731","10.1145\/3340555"],"URL":"https:\/\/doi.org\/10.1145\/3340555.3353731","relation":{},"subject":[],"published":{"date-parts":[[2019,10,14]]},"assertion":[{"value":"2019-10-14","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}