Comments (4)
Hi @mraduldubey ,
You are right, the character embeddings are indeed initialized randomly. However, at training time, the loss is backpropagated all the way and the character embeddings are thus updated (thus using supervised learning).
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Thanks @guillaumegenthial for the reply. This way the ground truth will be a vector representing the whole word. So, what is the ground truth here?
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You train the network to predict the tags. Turns out some parameters of the network correspond to character embeddings, so these are trained to help the network predict the tags. So the ground truth is the tag, and the learned embeddings help predict this tag.
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So, you mean that the word representation n/w, the contextual word representation n/w and the decoder, though mentioned separately in the blog, are trained simultaneously in conjunction with the ground truth being the tags and the backpropagation happens from the final layer back to the word representation n/w.
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Related Issues (20)
- InvalidArgumentError HOT 1
- Question: Effect of missing word in pre-trained word embeddings on model performance. HOT 1
- Is the evaluation metric the same as the ones in the papers?
- Does this evaluation script apply to BIO or BIES? HOT 1
- The pred_ids of `<pad>` is always zero
- 0 precision 0 recall for some custom tags HOT 2
- Why the result is better than that in the papers? #87 HOT 1
- batch size is creating confusion [ when we compare with Research Paper ]
- tensorflow.contrib.estimator HOT 6
- For models/lstm_crf/main.py, Line 171
- Support for TF 2.0 HOT 6
- InvalidArgumentError: labels contains negative values
- has no attribute 'stop_if_no_increase_hook' for tensorflow 1.9 HOT 1
- Which version of numpy will work HOT 1
- There are issues when I use my own datasets HOT 1
- Thanks for your amazing work!
- Visualizing embeddings & improving accuracy
- In reported result, what is difference between best and abs. best ? Also mean + std. deviation doesnt matches with the best result reported in the github page ? HOT 1
- same result on F1, Accuracy, precision
- Prediction script for single line statements
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