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Machine Comprehension Using Commonsense Knowledge
 Daniš, Tomáš ; Landini, Federico Nicolás (oponent) ; Fajčík, Martin (vedoucí práce)
In this thesis, the commonsense reasoning ability of modern neural systems is explored. The goal is to provide insight into the current state of research in this area and identify promising research directions. A state-of-the-art question-answering model has been implemented and experimented with in various scenarios. Unlike in older approaches, the model achieved comparable results with best available models for the target task without using any task-specific architecture. Furthermore, unintended statistical biases are discovered in a popular commonsense reasoning dataset which allow models to compute the correct answer even when it does not have sufficient information to do so. Based on these findings, recommendations and possible future research areas are suggested.
Úplný záznam
Machine Comprehension Using Commonsense Knowledge
 Daniš, Tomáš ; Landini, Federico Nicolás (oponent) ; Fajčík, Martin (vedoucí práce)
In this thesis, the commonsense reasoning ability of modern neural systems is explored. The goal is to provide insight into the current state of research in this area and identify promising research directions. A state-of-the-art question-answering model has been implemented and experimented with in various scenarios. Unlike in older approaches, the model achieved comparable results with best available models for the target task without using any task-specific architecture. Furthermore, unintended statistical biases are discovered in a popular commonsense reasoning dataset which allow models to compute the correct answer even when it does not have sufficient information to do so. Based on these findings, recommendations and possible future research areas are suggested.
Úplný záznam

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