Jussi Karlgren

Generative-language model-training instructions must be created to fit the cultural constraints relevant to where the model is intended to be used.

In this paper, we propose a novel way of representing graphs for processing in Graph Neural Networks. We reduce the dimensionality of the input data by using Random Indexing, a Vector Symbolic Architectural framework; we implement a new trainable neural layer, also inspired by Vector Symbolic Architectures; we leverage the sparseness of the incoming data in a Sparse Neural Network framework. Our experiments on a number of publicly available datasets and standard benchmarks demonstrate that we can reduce the number of parameters by up to two orders of magnitude. We show how this parsimonious approach not only delivers competitive results but even improves performance for node classification and link prediction. We find that this holds in particular for cases where the graph lacks node features.

In this work, we study the association between song lyrics and mood through a data-driven analysis. Our data set consists of nearly one million songs, with song-mood associations derived from user playlists on the Spotify streaming platform. We take advantage of state-of-the-art natural language processing models based on transformers to learn the association between the lyrics and moods. We find that a pretrained transformer-based language model in a zero-shot setting -- i.e., out of the box with no further training on our data -- is powerful for capturing song-mood associations. Moreover, we illustrate that training on song-mood associations results in a highly accurate model that predicts these associations for unseen songs. Furthermore, by comparing the prediction of a model using lyrics with one using acoustic features, we observe that the relative importance of lyrics for mood prediction in comparison with acoustics depends on the specific mood. Finally, we verify if the models are capturing the same information about lyrics and acoustics as humans through an annotation task where we obtain human judgments of mood-song relevance based on lyrics and acoustics.