In this project I studied how generative neural language models can be used for response generation. The purpose of the model is to generate responses for a social robot, instead of having responses be authored and evaluated by crowd-sourced workers. To achieve this task, I train a large-scale pre-trained neural language model on the collected data. I trained six model variations to study the changes in utterance quality, the models vary in the amount of pre-training they have. I also test three different decoding methods for the same purpose. One of the model variations utilize multi-task learning during training, where the model performs other tasks alongside response generation. The utterances produced by the models were evaluated through crowd-sourced human evaluation. Utterances were shown by the evaluation to be of roughly equal quality to the original utterances it was trained to replicate. The results show that a large-scale language model may be a viable alternative to crowd-sourced authoring and evaluation of utterances, reducing costs and providing more reliable results.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:uu-415323 |
| Date | January 2020 |
| Creators | Nyberg, Jakob |
| 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 |
| Relation | UPTEC IT, 1401-5749 ; 20027 |
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