Deep contextualized word representations (ELMo)

We introduce a new type of deep contextualized word representation that models both (1) complex characteristics of word use (e.g., syntax and semantics), and (2) how these uses vary across linguistic contexts (i.e., to model polysemy). Our word vectors are learned functions of the internal states of a deep bidirectional language model (biLM), which is pre-trained on a large text corpus. We show that these representations can be easily added to existing models and significantly improve the state of the art across six challenging NLP problems, including question answering, textual entailment and sentiment analysis. We also present an analysis showing that exposing the deep internals of the pre-trained network is crucial, allowing downstream models to mix different types of semi-supervision signals.

Three Important Things

1. Context-aware Word Embeddings

Most word embedding techniques only embed single words without regard to their context, which could affect the performance of downstream tasks. ELMo (Embeddings from Language Models) instead embeds words as a function of the entire input sentence using a two-layer bidirectional language model (biLM).

2. Using All biLM Layers For Representation

Previous work only used the last layer of the biLM. However, it was empirically observed that using all layers (by a task-specific weighing of each layer that decays geometrically) achieved better results.

This is because different layers represent different information and so including all biLM layers helps in downstream tasks.

3. Better Sample Efficiency

It was observed empirically that using ELMo can improve sample efficiency significantly.

Most Glaring Deficiency

Reasons for why ELMO achieves better sample efficiency were not discussed or hypothesized, unlike why utilizing ELMo obtained better results on many NLP tasks than state-of-the-art. Postulating some hypotheses to answer this question for future research directions would have been helpful.

Conclusions for Future Work

Context-aware representations can improve performance. Even intermediate layers of a final representation could be useful for downstream tasks to take advantage of.