concerning the fist question, I’m using only three dimension to simplify the annotation process. This space could have more dimensions, offering a more rich description at emotional level.
Concerning the second question, in the examples the emotional values were shown at the token (word) level. However, this is a simplified representation of a more complex process. While individual tokens have their own emotional embeddings, these are not used in isolation. The model integrates these token-level embeddings with their context. This integration happens through the attention mechanism, which considers the relationships between all tokens in a sequence.
The overall emotional evaluation of a sentence arises from the interaction of its individual tokens through the attention mechanism. This enables the model to capture subtle emotional variations that result from the combining of words, which may deviate from a simple aggregation of individual word emotions. The λ parameter in our attention mechanism allows the model to adaptively weight the importance of emotional information relative to semantic content.
Thank you for your response! That clears things up a bit.
So in essence what you are proposing is modifying the Transformer architecture for processing emotional valuation alongside semantic meanings. Both start out as per-token embeddings, and are then updated via their respective attention mechanisms and NLP layers.
I’m not sure if I have the whole picture, or even if what I wrote above is a correct model of your proposal. I think my biggest confusion is this:
Are the semantic and emotional information flows fully parallel, or do they update each other along the way?
While semantic and emotional information flows start in parallel, they are not fully parallel throughout the entire process. They update each other iteratively, enabling it to capture intricate connections between semantic content and emotional tone. This has the potential to enhance the model’s comprehension of the input text, resulting in a more refined understanding.
I have some questions about this:
- Why three dimensions exactly?
- Is the “emotional value” assigned per token or per sentence?
Hi Milan,
concerning the fist question, I’m using only three dimension to simplify the annotation process. This space could have more dimensions, offering a more rich description at emotional level.
Concerning the second question, in the examples the emotional values were shown at the token (word) level. However, this is a simplified representation of a more complex process. While individual tokens have their own emotional embeddings, these are not used in isolation. The model integrates these token-level embeddings with their context. This integration happens through the attention mechanism, which considers the relationships between all tokens in a sequence.
The overall emotional evaluation of a sentence arises from the interaction of its individual tokens through the attention mechanism. This enables the model to capture subtle emotional variations that result from the combining of words, which may deviate from a simple aggregation of individual word emotions. The λ parameter in our attention mechanism allows the model to adaptively weight the importance of emotional information relative to semantic content.
Thank you for your response! That clears things up a bit.
So in essence what you are proposing is modifying the Transformer architecture for processing emotional valuation alongside semantic meanings. Both start out as per-token embeddings, and are then updated via their respective attention mechanisms and NLP layers.
I’m not sure if I have the whole picture, or even if what I wrote above is a correct model of your proposal. I think my biggest confusion is this:
Are the semantic and emotional information flows fully parallel, or do they update each other along the way?
While semantic and emotional information flows start in parallel, they are not fully parallel throughout the entire process. They update each other iteratively, enabling it to capture intricate connections between semantic content and emotional tone. This has the potential to enhance the model’s comprehension of the input text, resulting in a more refined understanding.