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Understanding the Importance of Entities and Roles in Natural Language Inference : A Model and Datasets

Abstract In this thesis, I present two new datasets and a modification to the existing models in the form of a novel attention mechanism for Natural Language Inference (NLI). The new datasets have been carefully synthesized from various existing corpora released for different tasks.

The task of NLI is to determine the possibility of a sentence referred to as “Hypothesis” being true given that another sentence referred to as “Premise” is true. In other words, the task is to identify whether the “Premise” entails, contradicts or remains neutral with regards to the “Hypothesis”. NLI is a precursor to solving many Natural Language Processing (NLP) tasks such as Question Answering and Semantic Search. For example, in Question Answering systems, the ques... (more)
Created Date 2019
Contributor Shrivastava, Ishan (Author) / Baral, Chitta (Advisor) / Anwar, Saadat (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Subject Artificial intelligence / Computer science / Information technology / Artificial Intelligence / Deep Learning / Entailment / Natural Language Inference / Natural Language Processing / Natural Language Understanding
Type Masters Thesis
Extent 106 pages
Language English
Note Masters Thesis Computer Science 2019
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS

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Description Dissertation/Thesis