Welcome to our final screencast on distributed word representations! In this session, we will explore the topic of deriving static representations from contextual models. This concept may sound specific, but it has the potential to be truly empowering as you develop your own systems. So let’s dive in!
Contents
Contextual vs. Static Representations
Contextual models like BERT, RoBERTa, XLNet, and ELECTRA are designed to provide contextual representations of words. This means that the representations of individual words can vary depending on the specific context in which they appear. On the other hand, static representations of words remain the same regardless of context.
The question then arises: Can we derive static representations from the contextual ones provided by models like BERT? The answer, according to Bommasani et al, is a resounding yes. There are effective methods for obtaining static representations from these contextual models, and that is the focus of this screencast.
Understanding BERT
BERT, as an example of a contextual model, processes sequences by first passing them through an embedding layer and then multiple additional layers. These layers produce vector representations, which undergo extensive computations.
One key feature of BERT is its contextual nature. Different sequences and even individual tokens within those sequences receive unique representations due to their varying context and position. The class and SEP tokens may share the same embedding, but overall, the representations differ significantly.
Tokenizing Sequences with BERT
BERT tokenizes sequences by breaking words into subword tokens, allowing for a smaller vocabulary compared to models like GloVe. You can use Hugging Face’s library to experiment with BERT tokenization.
Deriving Static Representations
There are two primary approaches to deriving static representations: the decontextualized approach and the aggregated approach.
Decontextualized Approach
In the decontextualized approach, individual words are processed as separate sequences. These words are tokenized into subword tokens, and the resulting grid of representations is then pooled using a function like mean, max, min, or last to obtain a fixed static representation for each word.
While this approach may seem straightforward, it may be unnatural for contextual models like BERT, which are trained on full sequences. However, the decontextualized approach can still yield promising results.
Aggregated Approach
The aggregated approach involves processing multiple corpus examples containing the target word. These examples, along with the target word itself, are tokenized into subword tokens. Pooled representations are obtained for each example, and then an average representation is calculated across all the examples. This approach leverages the strengths of contextual models and can yield more natural static representations.
Results and Conclusions
Extensive research by Bommasani et al reveals that lower layers in contextual models tend to provide better word-level discrimination. Mean pooling emerges as a consistently effective pooling function for both contexts and subwords.
Overall, the aggregated approach outperforms the decontextualized approach. However, if computational resources are limited, the simpler decontextualized approach with mean pooling remains a competitive choice.
In conclusion, deriving static representations from contextual models is indeed possible and offers exciting prospects. By following the approaches discussed here, you can unlock the potential of contextual models like BERT in developing your own systems.
FAQs
1. Can I use the decontextualized approach even though it may be unnatural for contextual models?
Yes, the decontextualized approach can still yield promising results, even though it may not align perfectly with the nature of contextual models like BERT. It is a simple and computationally efficient method that can provide competitive static representations.
2. What is the best pooling function to use for deriving static representations?
Mean pooling consistently emerges as the best choice for both context and subword representations. It effectively captures the essence of the representations and yields strong results.
3. Is the aggregated approach computationally demanding?
Yes, the aggregated approach requires processing a vast number of corpus examples, making it computationally demanding. However, the results obtained through this approach generally outperform the decontextualized approach.
Conclusion
Deriving static representations from contextual models like BERT opens up new possibilities in developing systems. By leveraging the decontextualized or aggregated approach and employing suitable pooling functions, you can obtain static representations that empower your applications. So go ahead and explore the power of contextual models to transform the world of technology!
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