Attention Mechanisms: Stop Compressing, Start Looking Back
This article discusses the limitations of RNNs and how attention mechanisms address them by allowing the decoder to dynamically focus on relevant parts of the input sequence during generation.
Why it matters
Attention mechanisms have been a key innovation in neural networks, enabling significant improvements in tasks like machine translation and language modeling.
Key Points
- 1RNNs compress the entire input sequence into a single fixed-size vector, leading to information loss for long inputs
- 2Attention allows the decoder to access the full sequence of encoder states and attend to the most relevant ones when generating each output
- 3Attention helps address the word order problem by allowing the decoder to reorder the input sequence as needed
Details
The article uses the author's personal experience of learning to write in English as an analogy to explain the problems that attention mechanisms solve. When translating from Tamil to English, the author would first compose a full paragraph in Tamil, then try to hold the 'compressed summary' of that paragraph in their working memory while translating word-by-word to English. This led to losing context and drifting from the original thought. This is analogous to how RNN encoders compress the entire input sequence into a single vector. Attention addresses this by allowing the decoder to access the full sequence of encoder states and attend to the most relevant ones when generating each output. Additionally, attention helps with the word order problem, as the decoder can dynamically reorder the input sequence to match the target language structure, rather than being constrained to left-to-right generation.
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