Fine-tune Bert for DocRED with Two-step Process
Table 3: Comparison of the BiLSTM baseline with SentModel which encode the document sentence by sentence. We report the F1 score and AUC on the Dev set here.
['Model', 'F1', 'AUC']
To test whether current model can capture the complex interaction between entities, we use a SentModel which encodes the document sentence by sentence. Then we locate each entity within a specific sentence and compute its embedding by averaging the word embedding of the entity name. In this way, there will be no interaction between sentences since we encode the whole document sentence by sentence. Surprisingly, the SentModel can achieve very similar performance compared to the BiLSTM model which encodes the whole document as a sequence. Therefore, the current model fails to capture complex interactions among entities, and only local information around each entity is used to predict a relation.