Matching Items (2)
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Description
This research lays down foundational work in the semantic reconstruction of linguistic politeness in English-to-Japanese machine translation and thereby advances semantic-based automated translation of English into other natural languages. I developed a Java project called the PoliteParser that is intended as a plug-in to existing semantic parsers to determine whether

This research lays down foundational work in the semantic reconstruction of linguistic politeness in English-to-Japanese machine translation and thereby advances semantic-based automated translation of English into other natural languages. I developed a Java project called the PoliteParser that is intended as a plug-in to existing semantic parsers to determine whether verbs in dialogue in an English corpus should be conjugated into the plain or the polite honorific form when translated into Japanese. The PoliteParser bases this decision off of semantic information about the social relationships between the speaker and the listener, the speaker's personality, and the circumstances of the utterance. Testing undergone during the course of this research demonstrates that the PoliteParser can achieve levels of accuracy 31 percentage points higher than that of statistical translation systems when integrated with a semantic parser and 54 percentage points higher when used with pre-parsed data.
ContributorsGuiou, Jared Tyler (Author) / Baral, Chitta (Thesis director) / Tanno, Koji (Committee member) / School of International Letters and Cultures (Contributor) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
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Description
Increasing misinformation in social media channels has become more prevalent since the beginning of the COVID-19 pandemic as countless myths and rumors have circulated over the internet. This misinformation has potentially lethal consequences as many people make important health decisions based on what they read online, thus creating an urgent

Increasing misinformation in social media channels has become more prevalent since the beginning of the COVID-19 pandemic as countless myths and rumors have circulated over the internet. This misinformation has potentially lethal consequences as many people make important health decisions based on what they read online, thus creating an urgent need to combat it. Although many Natural Language Processing (NLP) techniques have been used to identify misinformation in text, prompt-based methods are under-studied for this task. This work explores prompt learning to classify COVID-19 related misinformation. To this extent, I analyze the effectiveness of this proposed approach on four datasets. Experimental results show that prompt-based classification achieves on average ~13% and ~6% improvement compared to a single-task and multi-task model, respectively. Moreover, analysis shows that prompt-based models can achieve competitive results compared to baselines in a few-shot learning scenario.
ContributorsBrown, Clinton (Author) / Baral, Chitta (Thesis director) / Walker, Shawn (Committee member) / Barrett, The Honors College (Contributor) / School of International Letters and Cultures (Contributor) / Computer Science and Engineering Program (Contributor)
Created2022-05