Source Embeddings

Source embeddings integrate information from multiple pre-trained word or audio embeddings to create a more comprehensive and accurate representation. Current research focuses on developing effective methods for combining these source embeddings, including weighted concatenation and meta-learning approaches that preserve important relationships between different sense representations. This research is significant because improved source embeddings enhance performance in downstream tasks such as word sense disambiguation, audio source separation, and price prediction, while also highlighting and addressing issues like gender bias present in the underlying data.