You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
I have one doubt over your code: in your code, all OOV words are represented by id 1, which means, all OOV words are considered the same word, and its embedding is a zero vector. Also, this embedding will not be updated during training. However, in the original paper, the author mentioned that for OOV words, the word embeddings are updated during training.
I think this may be a reason why the score is lower than the original paper.
The text was updated successfully, but these errors were encountered:
Hello,
I have one doubt over your code: in your code, all OOV words are represented by id 1, which means, all OOV words are considered the same word, and its embedding is a zero vector. Also, this embedding will not be updated during training. However, in the original paper, the author mentioned that for OOV words, the word embeddings are updated during training.
I think this may be a reason why the score is lower than the original paper.
The text was updated successfully, but these errors were encountered: