Language model-based embeddings

Language model-based embeddings represent the next step in the evolution of word embeddings. A language model is a probability distribution over sequences of words. Once we have a model, we can ask it to predict the most likely next word given a particular sequence of words. Similar to traditional word embeddings, both static and dynamic, they are trained to predict the next word (or previous word as well, if the language model is bidirectional) given a partial sentence from the corpus. Training does not involve active labeling, since it leverages the natural grammatical structure of large volumes of text, so in a sense this is an unsupervised learning process:

Figure 4: Different stages of training ULMFit embeddings (Howard and Ruder, 2018)

The main difference between a language model as a word embedding and more traditional embeddings is that traditional embeddings are applied as a single initial transformation on the data, and are then fine...