We propose a neural network-based system for automatically grading essays written in Swedish. Previous system either relies on laboriously crafted features extracted by human experts or are limited to essays written in English. By using different variations of Long Short-Term Memory (LSTM) networks, our system automatically learns the relation between Swedish high-school essays and their assigned score. Using all of the intermediate states from the LSTM network proved to be crucial in order to understand the essays. Furthermore, we evaluate different ways of representing words as dense vectors which ultimately have a substantial effect on the overall performance. We compare our results to the ones achieved by the first and previously only automatic essay scoring system designed for the Swedish language. Although no state-of-the-art performance is reached, indication of the potential from a neural based grading system is found.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:uu-352505 |
| Date | January 2018 |
| Creators | Lilja, Mathias |
| Publisher | Uppsala universitet, Statistiska institutionen |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
Page generated in 0.0025 seconds