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.
ELMo introduces a new form of word representation that accounts for the complexity of language by providing deep, context-dependent vectors.
It is based on a bi-directional Language Model (biLM) and utilizes all internal states of the language model to create word vectors.
ELMo layers are combined linearly, allowing for a rich blend of representations that capture both syntactic nuances and semantic content.
Incorporating ELMo into existing NLP models results in substantial performance enhancements across various NLP tasks.
ELMo reduces the amount of training data needed and encodes information ranging from syntax to semantics, proving the significance of having representations from all biLM layers.
In the realm of NLP, the effectiveness of models relies heavily on the representation of words. Traditional methods assign a fixed vector to each word, which can be limiting due to the complexity of language. The research presented in this article introduces ELMo (Embeddings from Language Models), a novel way to create word representations that are not only deep, with multiple layers, but also context-dependent, capturing the variances in word meanings based on linguistic context.
The distinctiveness of ELMo lies in its ability to model words as functions of the entire linguistic context they appear in. Developed as part of a bi-directional Language Model (biLM), ELMo word vectors are computed based on all the internal states of the language model, rather than just leveraging the final output state.
A key aspect of ELMo is that it integrates information from all neural network layers within the pre-trained language model. This process can be likened to mixing a vast array of seasonings to hone the perfect flavor, as ELMo combines these layers linearly, adjusting the mix to suit the task at hand, permitting a rich variety of word representations. For instance, lower network layers tend to capture syntactical nuances, whereas higher layers are more adept at encapsulating semantic content.
When ELMo's deep representations are applied to existing state-of-the-art NLP models, they result in significant performance improvements. This has been proven across six challenging tasks, which include sentiment analysis, textual entailment, and question answering, among others. The effect of adding ELMo's representations alone was dramatic, frequently leading to relative error reductions of up to 20%.
Incorporating ELMo into various models is a straightforward process. One starts by freezing the pre-trained biLM's weights and generating representations for all input words. These are then linearly combined to create ELMo vectors that are appended to the base layers of the supervised model.
Performance isn’t the only area where ELMo excels. The research illustrates its efficiency in terms of sample usage, greatly reducing the quantity of training data or updates necessary to reach a particular level of performance. Moreover, the study showcases the mixture of information encoded by ELMo's representations, from syntactic to semantic dimensions, which underscores why all layers of the biLM are crucial to optimal task performance.
In conclusion, ELMo leads to discernible improvements in diverse NLP tasks by infusing models with deep contextualized word representations. Its approach offers a significant step forward in preparing language models that comprehend the fluidity and subtlety of human language.