The Impact of Word Splitting on the Semantic Content of Contextualized Word Representations

When deriving contextualized word representations from language models, adecision needs to be made on how to obtain one for out-of-vocabulary (OOV)words that are segmented into subwords. What is the best way to represent thesewords with a single vector, and are these representations of worse quality thanthose of in-vocabulary words? We carry out an intrinsic evaluation ofembeddings from different models on semantic similarity tasks involving OOVwords. Our analysis reveals, among other interesting findings, that the qualityof representations of words that are split is often, but not always, worse thanthat of the embeddings of known words. Their similarity values, however, mustbe interpreted with caution.