如何将bert的embeddins向量与其他特征结合起来？

Question

I am working on a classification task with 3 labels (0,1,2 = neg, pos, neu). Data are sentences. So to produce vectors/embeddings of sentences, I use a Bert encoder to get embeddings for each sentence and then I used a simple knn to make predictions.

My data look like this: each sentence has a label and other numerical value of classification.

For example, my data look like this

Sentence embeddings_BERT level sub-level label

je mange  [0.21, 0.56]    2     2.1      pos
il hait   [0.25, 0.39]   3     3.1      neg
.....

As you can see each sentence has other categories but the are not the final one but indices to help figure the label when a human annotated the data. I want my model to take into consideration those two values when predicting the label. I was wondering if I have to concatenate them with the embeddings generate by the bert encoding or is there another way?

Answer 1

There is not one perfect way to tackle this problem, but a simple solution will be to concat the bert embeddings with hard-coded features. The BERT embeddings (sentence embeddings) will be of dimension 768 (if you have used BERT base). These embeddings can be treated as features of the sentence itself. The additional features can be concatenated to form a higher dimensional vector. If the features are categorical, it will be ideal to convert to one-hot vectors and concatenate them. For example, if you want to use level in your example as set of input features, it will be best to convert it into one-hot feature vector and then concatenate with BERT embeddings. However, in some cases, your hard coded features can be dominant feature to bias the classifier, and in some other cases, it can have no influence at all. It all depends on the data that you have.