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Asian Low-Resour. Lang. Inf. Process."],"published-print":{"date-parts":[[2021,7,31]]},"abstract":"<jats:p>The preservation of domain knowledge from source to the target is crucial in any translation workflows. Hence, translation service providers that use machine translation (MT) in production could reasonably expect that the translation process should transfer both the underlying pragmatics and the semantics of the source-side sentences into the target language. However, recent studies suggest that the MT systems often fail to preserve such crucial information (e.g., sentiment, emotion, gender traits) embedded in the source text in the target. In this context, the raw automatic translations are often directly fed to other natural language processing (NLP) applications (e.g., sentiment classifier) in a cross-lingual platform. Hence, the loss of such crucial information during the translation could negatively affect the performance of such downstream NLP tasks that heavily rely on the output of the MT systems.<\/jats:p>\n          <jats:p>\n            In our current research, we carefully balance both the sides (i.e., sentiment and semantics) during translation, by controlling a global-attention-based neural MT (NMT), to generate translations that encode the underlying sentiment of a source sentence while preserving its non-opinionated semantic content. Toward this, we use a state-of-the-art reinforcement learning method, namely,\n            <jats:italic>actor-critic<\/jats:italic>\n            , that includes a novel\n            <jats:italic>reward combination<\/jats:italic>\n            module, to fine-tune the NMT system so that it learns to generate translations that are best suited for a downstream task, viz. sentiment classification while ensuring the source-side semantics is intact in the process. Experimental results for Hindi\u2013English language pair show that our proposed method significantly improves the performance of the sentiment classifier and alongside results in an improved NMT system.\n          <\/jats:p>","DOI":"10.1145\/3450970","type":"journal-article","created":{"date-parts":[[2021,6,30]],"date-time":"2021-06-30T19:52:55Z","timestamp":1625082775000},"page":"1-27","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":8,"title":["Reinforced NMT for Sentiment and Content Preservation in Low-resource Scenario"],"prefix":"10.1145","volume":"20","author":[{"given":"Divya","family":"Kumari","sequence":"first","affiliation":[{"name":"Indian Institute of Technology Patna, India"}]},{"given":"Asif","family":"Ekbal","sequence":"additional","affiliation":[{"name":"Indian Institute of Technology Patna, India"}]},{"given":"Rejwanul","family":"Haque","sequence":"additional","affiliation":[{"name":"ADAPT Centre, School of Computing, National College of Ireland, Ireland"}]},{"given":"Pushpak","family":"Bhattacharyya","sequence":"additional","affiliation":[{"name":"Indian Institute of Technology Patna, India"}]},{"given":"Andy","family":"Way","sequence":"additional","affiliation":[{"name":"ADAPT Centre, Dublin City University, Ireland"}]}],"member":"320","published-online":{"date-parts":[[2021,6,30]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Proceedings of the 18th International Conference on Computational Linguistics and Intelligent Text Processing.","author":"Afli Haithem","year":"2017","unstructured":"Haithem Afli , Sorcha Maguire , and Andy Way . 2017 . Sentiment translation for low resourced languages: Experiments on Irish general election tweets . In Proceedings of the 18th International Conference on Computational Linguistics and Intelligent Text Processing. Haithem Afli, Sorcha Maguire, and Andy Way. 2017. Sentiment translation for low resourced languages: Experiments on Irish general election tweets. In Proceedings of the 18th International Conference on Computational Linguistics and Intelligent Text Processing."},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D19-1166"},{"key":"e_1_2_1_3_1","volume-title":"Proceedings of the 5th International Conference on Learning Representations.","author":"Bahdanau Dzmitry","year":"2017","unstructured":"Dzmitry Bahdanau , Philemon Brakel , Kelvin Xu , Anirudh Goyal , Ryan Lowe , Joelle Pineau , Aaron Courville , and Yoshua Bengio . 2017 . An actor-critic algorithm for sequence prediction . 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