Below are the abstract, introduction, and conclusion of a computer science research paper. Please summarize the main contribution of the work in a single sentence. Your response should include the summary and no additional text.

Paper text:

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 pretrained 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 pretrained network is crucial, allowing downstream models to mix different types of semi-supervision signals.

We have introduced a general approach for learning high-quality deep context-dependent representations from biLMs, and shown large improvements when applying ELMo to a broad range of NLP tasks.
Through ablations and other controlled experiments, we have also confirmed that the biLM layers efficiently encode different types of syntactic and semantic information about wordsin-context, and that using all layers improves overall task performance.Our approach raises several interesting questions for future work, broadly organized into two themes."What
is the best training regime for learning generally useful NLP representations?" By choosing
a biLM training objective, we benefit from nearly limitless unlabeled text and can immediately apply advances in language modeling, an active area of current research. However, it
's possible that further decreases in LM perplexity will not translate to more transferable representations, and that other objective functions might be more suitable for learning general purpose representations."What is the
best way to use deep contextual representations for other tasks?" Our method of
using a weighted average of all layers from the biLM is simple and empirically successful. However, a deeper
fusion of the biLM layers with a target NLP architecture may lead to further improvements.
We introduce a new type of deep contextualized word representation that significantly improves the state of the art for a range of challenging NLP tasks.