Generalized Language Models

As a follow up of word embedding post, we will discuss the models on learning contextualized word vectors, as well as the new trend in large unsupervised pre-trained language models which have achieved amazing SOTA results on a variety of language tasks. I guess they are Elmo & Bert? (Image source: here) We have seen amazing progress in NLP in 2018. Large-scale pre-trained language modes like OpenAI GPT and BERT have achieved great performance on a variety of language tasks using generic model architectures. The idea is similar to how ImageNet classification pre-training helps many vision tasks (*). Even better than vision classification pre-training, this simple and powerful approach in NLP does not require labeled data for pre-training, allowing us to experiment with increased training scale, up to our very limit. In my previous NLP post on word embedding, the introduced embeddings are not context-specific -- they are learned based on word concurrency but not sequential context.