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Multi task learning with general vector space for cross-lingual semantic relation detection
Sholikah R.W.a, Arifin A.Z.a, Fatichah C.a, Purwarianti A.b
a Department of Informatics, Faculty of Intelligent Electrical and Informatics Technology, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia
b Informatics, School of Electrical and Informatics, Institut Teknologi Bandung, Bandung, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2020Semantic relation detection has an important role in natural language processing. In a supervised approach, the training process requires a sufficient amount of labeled data. However, in low-resource languages, labeled data are limited, whereas in rich-resource languages, labeled data are available in large quantities. In addition, various studies tend to model the single-task problem without considering the generalization with other tasks. Hence, a strategy that can utilize the availability of labeled data in rich-resource languages and generalize models to improve the identification of relations in a cross-lingual manner is needed. In this paper, we propose a framework to identify cross-lingual semantic relation using multi-task learning with a general vector space. The proposed method was designed to construct a general vector space and semantic relation identification. The experiments were conducted over three datasets: Indonesian–Arabic, English–Arabic, and English–Indonesia. The results show that the use of multi-task learning with a general vector space can overcome the problem of cross-lingual semantic relation identification. This is shown by the accuracy of the synonym and hypernym tasks that reached 84.9% and 84.8%, respectively.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Cross-lingual semantic relation,General vector space,Hypernym,Multi-task learning,Synonym[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text]This work was supported by the Ministry of Research, Technology and Higher Education of Republic Indonesia under the PMDSU program.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1016/j.jksuci.2020.08.002[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]