Identifying Intensity of the Structure and Content in Tweets and the Discriminative Power of Attributes in Context with Referential Translation Machines

• URL: http://arxiv.org/abs/2407.05154v1
• Date: Sat, 6 Jul 2024 18:58:10 GMT
• Title: Identifying Intensity of the Structure and Content in Tweets and the Discriminative Power of Attributes in Context with Referential Translation Machines
• Authors: Ergun Biçici,
• Abstract summary: We use referential translation machines (RTMs) to identify the similarity between an attribute and two words in English. RTMs are also used to predict the intensity of the structure and content in tweets in English, Arabic, and Spanish.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We use referential translation machines (RTMs) to identify the similarity between an attribute and two words in English by casting the task as machine translation performance prediction (MTPP) between the words and the attribute word and the distance between their similarities for Task 10 with stacked RTM models. RTMs are also used to predict the intensity of the structure and content in tweets in English, Arabic, and Spanish in Task 1 where MTPP is between the tweets and the set of words for the emotion selected from WordNet affect emotion lists. Stacked RTM models obtain encouraging results in both.

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