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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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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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Abstract:
Expressing the polarity of sentiment as 'positive' and 'negative' usually have limited scope compared with the intensity/degree of polarity. These two tasks (i.e. sentiment classification and sentiment intensity prediction) are closely related and may offer assistance to each other during the learning process. In this paper, we propose to leverage the relatedness of multiple tasks in a multi-task learning framework. Our multi-task model is based on convolutional-Gated Recurrent Unit (GRU) framework, which is further assisted by a diverse hand-crafted feature set. Evaluation and analysis suggest that joint-learning of the related tasks in a multi-task framework can outperform each of the individual tasks in the single-task frameworks. ... : 12 pages, 3 figures and 3 tables. Accepted in 20th International Conference on Intelligent Text Processing and Computational Linguistics, CICLing 2019. To be published in Springer LNCS volume ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences; I.2.7
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URL: https://arxiv.org/abs/2002.02154 https://dx.doi.org/10.48550/arxiv.2002.02154
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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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