Modeling Actions and State Changes for a Machine Reading Comprehension Dataset
|Abstract||Artificial general intelligence consists of many components, one of which is Natural Language Understanding (NLU). One of the applications of NLU is Reading Comprehension where it is expected that a system understand all aspects of a text. Further, understanding natural procedure-describing text that deals with existence of entities and effects of actions on these entities while doing reasoning and inference at the same time is a particularly difficult task. A recent natural language dataset by the Allen Institute of Artificial Intelligence, ProPara, attempted to address the challenges to determine entity existence and entity tracking in natural text.
As part of this work, an attempt is made to address the ProPara challenge. The Knowledge... (more)
|Contributor||Bhattacharjee, Aurgho (Author) / Baral, Chitta (Advisor) / Yang, Yezhou (Committee member) / Anwar, Saadat (Committee member) / Arizona State University (Publisher)|
|Subject||Computer science / inductive logic programming / machine comprehension / machine reading comprehension / modeling actions / modeling states / natural language processing|
|Note||Masters Thesis Computer Science 2019|
|Collaborating Institutions||Graduate College / ASU Library|
|Additional Formats||MODS / OAI Dublin Core / RIS|