{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:28:53Z","timestamp":1750220933450,"version":"3.41.0"},"reference-count":33,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2019,5,21]],"date-time":"2019-05-21T00:00:00Z","timestamp":1558396800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Development of Knowledge Evolutionary WiseQA Platform Technology for Human Knowledge Augmented Services"},{"name":"Korea government","award":["2013-2-00131"],"award-info":[{"award-number":["2013-2-00131"]}]},{"name":"Institute for Information 8 Communications Technology Planning 8 Evaluation"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Asian Low-Resour. Lang. Inf. Process."],"published-print":{"date-parts":[[2019,12,31]]},"abstract":"<jats:p>\n            Quality estimation is an important task in machine translation that has attracted increased interest in recent years. A key problem in translation-quality estimation is the lack of a sufficient amount of the quality annotated training data. To address this shortcoming, the\n            <jats:italic>Predictor-Estimator<\/jats:italic>\n            was proposed recently by introducing \u201cword prediction\u201d as an additional pre-subtask that predicts a current target word with consideration of surrounding source and target contexts, resulting in a two-stage neural model composed of a\n            <jats:italic>predictor<\/jats:italic>\n            and an\n            <jats:italic>estimator<\/jats:italic>\n            . However, the original Predictor-Estimator is not trained on a continuous stacking model but instead in a cascaded manner that separately trains the predictor from the estimator. In addition, the Predictor-Estimator is trained based on single-task learning only, which uses target-specific quality-estimation data without using other training data that are available from other-level quality-estimation tasks. In this article, we thus propose a\n            <jats:italic>multi-task stack propagation<\/jats:italic>\n            , which extensively applies\n            <jats:italic>stack propagation<\/jats:italic>\n            to fully train the Predictor-Estimator on a continuous stacking architecture and\n            <jats:italic>multi-task learning<\/jats:italic>\n            to enhance the training data from related other-level quality-estimation tasks. Experimental results on WMT17 quality-estimation datasets show that the Predictor-Estimator trained with multi-task stack propagation provides statistically significant improvements over the baseline models. In particular, under an ensemble setting, the proposed multi-task stack propagation leads to state-of-the-art performance at all the sentence\/word\/phrase levels for WMT17 quality estimation tasks.\n          <\/jats:p>","DOI":"10.1145\/3321127","type":"journal-article","created":{"date-parts":[[2019,5,23]],"date-time":"2019-05-23T18:01:38Z","timestamp":1558634498000},"page":"1-18","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Multi-task Stack Propagation for Neural Quality Estimation"],"prefix":"10.1145","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5990-8158","authenticated-orcid":false,"given":"Hyun","family":"Kim","sequence":"first","affiliation":[{"name":"Electronics and Telecommunications Research Institute (ETRI), Daejeon, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jong-Hyeok","family":"Lee","sequence":"additional","affiliation":[{"name":"Pohang University of Science and Technology (POSTECH), Pohang, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Seung-Hoon","family":"Na","sequence":"additional","affiliation":[{"name":"Chonbuk National University, Jeonju, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2019,5,21]]},"reference":[{"volume-title":"Proceedings of the ICLR","year":"2015","author":"Bahdanau Dzmitry","key":"e_1_2_1_1_1"},{"key":"e_1_2_1_2_1","volume-title":"Findings of the 2017 conference on machine translation (WMT17). 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