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)
|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|
|Note||Masters Thesis Computer Science 2019|
|Collaborating Institutions||Graduate College / ASU Library|
|Additional Formats||MODS / OAI Dublin Core / RIS|