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Indian Language Wordnets and their Linkages with Princeton WordNet ...
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Techniques for Jointly Extracting Entities and Relations: A Survey ...
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Knowledge-based Extraction of Cause-Effect Relations from Biomedical Text ...
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How low is too low? A monolingual take on lemmatisation in Indian languages ...
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Role of Language Relatedness in Multilingual Fine-tuning of Language Models: A Case Study in Indo-Aryan Languages ...
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M2H2: A Multimodal Multiparty Hindi Dataset For Humor Recognition in Conversations ...
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"So You Think You're Funny?": Rating the Humour Quotient in Standup Comedy ...
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Role of Language Relatedness in Multilingual Fine-tuning of Language Models: A Case Study in Indo-Aryan Languages ...
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Crosslingual Embeddings are Essential in UNMT for Distant Languages: An English to IndoAryan Case Study ...
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Harnessing Cross-lingual Features to Improve Cognate Detection for Low-resource Languages ...
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Modelling source- and target-language syntactic Information as conditional context in interactive neural machine translation
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In: Gupta, Kamal Kumar, Haque, Rejwanul orcid:0000-0003-1680-0099 , Ekbal, Asif, Bhattacharyya, Pushpak and Way, Andy orcid:0000-0001-5736-5930 (2020) Modelling source- and target-language syntactic Information as conditional context in interactive neural machine translation. In: Proceedings of the 22nd Annual Conference of the European Association for Machine Translation, 2-6 Nov 2020, Lisboa, Portugal. (2020)
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Abstract:
In interactive machine translation (MT), human translators correct errors in auto- matic translations in collaboration with the MT systems, which is seen as an effective way to improve the productivity gain in translation. In this study, we model source- language syntactic constituency parse and target-language syntactic descriptions in the form of supertags as conditional con- text for interactive prediction in neural MT (NMT). We found that the supertags significantly improve productivity gain in translation in interactive-predictive NMT (INMT), while syntactic parsing somewhat found to be effective in reducing human efforts in translation. Furthermore, when we model this source- and target-language syntactic information together as the con- ditional context, both types complement each other and our fully syntax-informed INMT model shows statistically significant reduction in human efforts for a French– to–English translation task in a reference- simulated setting, achieving 4.30 points absolute (corresponding to 9.18% relative) improvement in terms of word prediction accuracy (WPA) and 4.84 points absolute (corresponding to 9.01% relative) reduc- tion in terms of word stroke ratio (WSR) over the baseline.
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Keyword:
Computational linguistics; Interactive Neural Machine Translation; Machine learning; Machine translating; Neural Machine Translation
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URL: http://doras.dcu.ie/24420/
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Syntax-informed interactive neural machine translation
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In: Gupta, Kamal Kumar, Haque, Rejwanul orcid:0000-0003-1680-0099 , Ekbal, Asif, Bhattacharyya, Pushpak and Way, Andy orcid:0000-0001-5736-5930 (2020) Syntax-informed interactive neural machine translation. In: The International Joint Conference on Neural Networks (IJCNN), 19-24 July 2020, Glasgow, UK (Online). (2020)
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Extracting N-ary Cross-sentence Relations using Constrained Subsequence Kernel ...
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Related Tasks can Share! A Multi-task Framework for Affective language ...
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Reinforced Multi-task Approach for Multi-hop Question Generation ...
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Utilizing Language Relatedness to improve Machine Translation: A Case Study on Languages of the Indian Subcontinent ...
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Harnessing Cross-lingual Features to Improve Cognate Detection for Low-resource Languages ...
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