Implementation of Q&A dialogue system - Výboch, Peter (2021)
Recent neural network architectures, transformers, enabled development of powerful language models pretrained on large sample of language data. Such models can be fine-tuned for downstream tasks. While fine-tuning is not nearly as effortful as training a model from scratch, for complex tasks which require dramatic alterations to the input, it can prove to be effortful. In our work we explore how task of knowledge-base question answering can be cast for a stock RoBERTa transformer pretrained for STS without any fine tuning.