{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T18:56:02Z","timestamp":1768589762566,"version":"3.49.0"},"publisher-location":"New York, NY, USA","reference-count":36,"publisher":"ACM","license":[{"start":{"date-parts":[[2018,7,19]],"date-time":"2018-07-19T00:00:00Z","timestamp":1531958400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2018,7,19]]},"DOI":"10.1145\/3219819.3219870","type":"proceedings-article","created":{"date-parts":[[2018,7,19]],"date-time":"2018-07-19T13:05:12Z","timestamp":1532005512000},"page":"636-645","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":11,"title":["Multi-Task Learning with Neural Networks for Voice Query Understanding on an Entertainment Platform"],"prefix":"10.1145","author":[{"given":"Jinfeng","family":"Rao","sequence":"first","affiliation":[{"name":"Comcast Applied AI Research Lab, Washington, DC &University of Maryland, College Park, MD, USA"}]},{"given":"Ferhan","family":"Ture","sequence":"additional","affiliation":[{"name":"Comcast Applied AI Research Lab, Washington, DC, USA"}]},{"given":"Jimmy","family":"Lin","sequence":"additional","affiliation":[{"name":"University of Waterloo, Waterloo, ON, Canada"}]}],"member":"320","published-online":{"date-parts":[[2018,7,19]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1007379606734"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/1526709.1526711"},{"key":"e_1_3_2_1_3_1","volume":"201","author":"Chelba C.","unstructured":"C. Chelba and J. Schalkwyk. 201 3. Empirical Exploration of Language Modeling for the google.com Query Stream as Applied to Mobile Voice Search. Mobile Speech and Advanced Natural Language Solutions. C. Chelba and J. Schalkwyk. 2013. Empirical Exploration of Language Modeling for the google.com Query Stream as Applied to Mobile Voice Search. Mobile Speech and Advanced Natural Language Solutions.","journal-title":"J. Schalkwyk."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/1390156.1390177"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"crossref","unstructured":"M. Dundar Q. Kou B. Zhang Y. He and B. Rajwa. 2015. Simplicity of K-means Versus Deepness of Deep Learning: A Case of Unsupervised Feature Learning with Limited Data. ICMLA. 883--888.  M. Dundar Q. Kou B. Zhang Y. He and B. Rajwa. 2015. Simplicity of K-means Versus Deepness of Deep Learning: A Case of Unsupervised Feature Learning with Limited Data. ICMLA. 883--888.","DOI":"10.1109\/ICMLA.2015.78"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"crossref","unstructured":"J. Feng and S. Bangalore. 2009. Effects of Word Confusion Networks on Voice Search. EACL. 238--245.   J. Feng and S. Bangalore. 2009. Effects of Word Confusion Networks on Voice Search. EACL. 238--245.","DOI":"10.3115\/1609067.1609093"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.3115\/1219840.1219885"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/2983323.2983769"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/2911451.2911525"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/2806416.2806491"},{"key":"e_1_3_2_1_11_1","volume":"201","author":"He H.","unstructured":"H. He , J. Wieting , K. Gimpel , J. Rao , and J. Lin. 201 6. UMD-TTIC-UW at SemEval-2016 Task 1: Attention-Based Multi-Perspective Convolutional Neural Networks for Textual Similarity Measurement. SemEval. 1103--1108. H. He, J. Wieting, K. Gimpel, J. Rao, and J. Lin. 2016. UMD-TTIC-UW at SemEval-2016 Task 1: Attention-Based Multi-Perspective Convolutional Neural Networks for Textual Similarity Measurement. SemEval. 1103--1108.","journal-title":"J. Lin."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/2505515.2505665"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/2484028.2484092"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/1150402.1150429"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"crossref","unstructured":"A. Joulin E. Grave P. Bojanowski and T. Mikolov. 2016. Bag of Tricks for Efficient Text Classification. arXiv:1607.01759.  A. Joulin E. Grave P. Bojanowski and T. Mikolov. 2016. Bag of Tricks for Efficient Text Classification. arXiv:1607.01759.","DOI":"10.18653\/v1\/E17-2068"},{"key":"e_1_3_2_1_16_1","unstructured":"J. Lafferty A. McCallum and F. Pereira. 2001. Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data. ICML. 282--289.   J. Lafferty A. McCallum and F. Pereira. 2001. Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data. ICML. 282--289."},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"crossref","unstructured":"P. Liu X. Qiu and X. Huang. 2017. Adversarial Multi-task Learning for Text Classification. arXiv:1704.05742.  P. Liu X. Qiu and X. Huang. 2017. Adversarial Multi-task Learning for Text Classification. arXiv:1704.05742.","DOI":"10.18653\/v1\/P17-1001"},{"key":"e_1_3_2_1_18_1","unstructured":"M.-T. Luong Q. Le I. Sutskever O. Vinyals and L. Kaiser. 2015. Multi-task Sequence to Sequence Learning. arXiv:1511.06114.  M.-T. Luong Q. Le I. Sutskever O. Vinyals and L. Kaiser. 2015. Multi-task Sequence to Sequence Learning. arXiv:1511.06114."},{"key":"e_1_3_2_1_19_1","unstructured":"T. Mikolov W. Yih and G. Zweig. 2013. Linguistic Regularities in Continuous Space Word Representations. HLT\/NAACL. 746--751.  T. Mikolov W. Yih and G. Zweig. 2013. Linguistic Regularities in Continuous Space Word Representations. HLT\/NAACL. 746--751."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"crossref","unstructured":"R. Pasunuru and M. Bansal. 2017. Multi-Task Video Captioning with Video and Entailment Generation. arXiv:1704.07489.  R. Pasunuru and M. Bansal. 2017. Multi-Task Video Captioning with Video and Entailment Generation. arXiv:1704.07489.","DOI":"10.18653\/v1\/D17-1103"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/2983323.2983872"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/3077136.3080648"},{"key":"e_1_3_2_1_23_1","volume":"201","author":"Rao J.","unstructured":"J. Rao , H. He , H. Zhang , F. Ture , R. Sequiera , S. Mohammed , and J. Lin. 201 7. Integrating Lexical and Temporal Signals in Neural Ranking Models for Social Media Search. Neu-IR. J. Rao, H. He, H. Zhang, F. Ture, R. Sequiera, S. Mohammed, and J. Lin. 2017. Integrating Lexical and Temporal Signals in Neural Ranking Models for Social Media Search. Neu-IR.","journal-title":"J. Lin."},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/3132847.3132893"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/3209978.3210140"},{"key":"e_1_3_2_1_26_1","first-page":"i","volume":"201","author":"Rao J.","unstructured":"J. Rao , W. Yang , Y. Zhang , F. Ture , and J. Lin. 201 8. Mult i -Perspective Relevance Matching with Hierarchical ConvNets for Social Media Search. arXiv:1805.08159. J. Rao, W. Yang, Y. Zhang, F. Ture, and J. Lin. 2018. Multi-Perspective Relevance Matching with Hierarchical ConvNets for Social Media Search. arXiv:1805.08159.","journal-title":"J. Lin."},{"key":"e_1_3_2_1_27_1","volume":"201","author":"Sequiera R.","unstructured":"R. Sequiera , G. Baruah , Z. Tu , S. Mohammed , J. Rao , H. Zhang , and J. Lin. 201 7. Exploring the Effectiveness of Convolutional Neural Networks for Answer Selection in End-to-End Question Answering. Neu-IR. R. Sequiera, G. Baruah, Z. Tu, S. Mohammed, J. Rao, H. Zhang, and J. Lin. 2017. Exploring the Effectiveness of Convolutional Neural Networks for Answer Selection in End-to-End Question Answering. Neu-IR.","journal-title":"J. Lin."},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"crossref","unstructured":"J. Shan G. Wu Z. Hu X. Tang M. Jansche and P. Moreno. 2010. Search by Voice in Mandarin Chinese. INTERSPEECH. 354--357.  J. Shan G. Wu Z. Hu X. Tang M. Jansche and P. Moreno. 2010. Search by Voice in Mandarin Chinese. INTERSPEECH. 354--357.","DOI":"10.21437\/Interspeech.2010-129"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/2872427.2882994"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/1321440.1321528"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/2009916.2009934"},{"key":"e_1_3_2_1_32_1","unstructured":"Y. Wang D. Yu Y. Ju and A. Acero. 2008. An Introduction to Voice Search. IEEE Signal Processing Magazine 29--38.  Y. Wang D. Yu Y. Ju and A. Acero. 2008. An Introduction to Voice Search. IEEE Signal Processing Magazine 29--38."},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"crossref","unstructured":"R. Yu A. Li V. Morariu and L. Davis. 2017. Visual Relationship Detection with Internal and External Linguistic Knowledge Distillation. ICCV. 1974--1982.  R. Yu A. Li V. Morariu and L. Davis. 2017. Visual Relationship Detection with Internal and External Linguistic Knowledge Distillation. ICCV. 1974--1982.","DOI":"10.1109\/ICCV.2017.121"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"crossref","unstructured":"R. Yu H. Wang and L. Davis. 2018. ReMotENet: Efficient Relevant Motion Event Detection for Large-Scale Home Surveillance Videos. WACV.  R. Yu H. Wang and L. Davis. 2018. ReMotENet: Efficient Relevant Motion Event Detection for Large-Scale Home Surveillance Videos. WACV.","DOI":"10.1109\/WACV.2018.00183"},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1145\/3132847.3132873"},{"key":"e_1_3_2_1_36_1","unstructured":"Y. Zhang and Q. Yang. 2017. A Survey on Multi-Task Learning. arXiv:1707.08114.  Y. Zhang and Q. Yang. 2017. A Survey on Multi-Task Learning. arXiv:1707.08114."}],"event":{"name":"KDD '18: The 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","location":"London United Kingdom","acronym":"KDD '18","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"]},"container-title":["Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3219819.3219870","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3219819.3219870","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T02:07:20Z","timestamp":1750212440000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3219819.3219870"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,7,19]]},"references-count":36,"alternative-id":["10.1145\/3219819.3219870","10.1145\/3219819"],"URL":"https:\/\/doi.org\/10.1145\/3219819.3219870","relation":{},"subject":[],"published":{"date-parts":[[2018,7,19]]},"assertion":[{"value":"2018-07-19","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}