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.
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.