Neural Networks for Language: How Context Became a Learned Transformation

The article "Before RNNs: Early Neural Language Models and the Limits of Fixed Windows" discusses a pivotal phase in natural language processing (NLP) history when models transitioned from using frequency tables (n-grams) to learning representations through neural networks. This shift marked a significant advancement, but also highlighted limitations that would eventually lead to the development of Recurrent Neural Networks (RNNs).

Key Points:

1. From Counting Frequencies to Learning Representations:

   • Early NLP models relied on n-grams, which are sequences of words used to predict the next word based on frequency counts.
   • The introduction of neural language models allowed for a more nuanced approach where context was no longer just about counting nearby words but understanding their meaning through learned vector representations.

2. Limitations of Fixed Windows:

   • Neural models with fixed windows (a set number of previous words) were an improvement over n-grams, as they could capture local context and learn meaningful word embeddings.
   • However, these models still had a critical limitation: they couldn't handle long-range dependencies or maintain a continuous memory of the entire sentence.

3. The Need for Recurrence:

   • The fixed window approach was

Read the full article at Towards AI - Medium

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