Comments (3)
Hi! Thanks for your interests! A "second pre-trained encoder" means that we use a separate encoder for the relation model (instead of sharing the encoder with the entity model). Both relation encoder and entity encoder are BERT.
Sorry about the confusion! Let me know if you have any further questions!
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Hello! Thanks for your help! But I still have a question about it. As you mentioned, the additional inserted marker plays an important role in obtaining the relation representation. Here is the thing. As BERT haven't seen these markers before, so I find it really hard to understand how to use BERT to get reasonable representation for these markers. I guess there are some pre-trained tasks like MLM to help BERT see these special markers at first? Please correct me if I miss the point.
Thanks,
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You are right that BERT haven't seen these markers during pre-training. We don't have any special pre-training tasks for marker tokens. The representations of marker tokens are learned merely during fine-tuning on the downstream task (relation classification).
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Related Issues (20)
- Multiple issues HOT 2
- different F1 with the same seed HOT 2
- tensorflow版本 HOT 1
- About the relation in datasets HOT 1
- [Paper] What are "gold" entity and relationship types? HOT 2
- Provide full environment
- Input Data Format HOT 5
- How to load models into Python HOT 2
- some code problems reguarding run_relation_approx(get_features_from_file) HOT 2
- where is the code of Efficient Batch Computations
- Approximation Model Training & Inference HOT 1
- entity is S or O ?
- Further question of f1 and e2e_f1
- 版本库问题 HOT 1
- 版本库问题
- ACE dataset
- Training a model on a dataset that is not ace04, ace05, or scierc HOT 1
- training model for WLP -- stuck in suboptimal solution
- Input data format question for custom dataset !
- cuda out of memory
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