instruction:
In this task, you will be presented with a context from an academic paper and a question separated with a 
. You have to answer the question based on the context.
question:
BERT-ADA BIBREF33 is a domain-adapted BERT-based model proposed for the APC task, which fine-tuned the BERT-BASE model on task-related corpus. This model obtained state-of-the-art accuracy on the Laptops dataset. We build a joint model for the multi-task of ATE and APC based on the BERT-BASE model. After optimizing the model parameters according to the empirical result, the joint model based on BERT-BASE achieved hopeful performance on all three datasets and even surpassed other proposed BERT based improved models on some datasets, such as BERT-PT, AEN-BERT, SDGCN-BERT, and so on. 
 Question: What was state of the art on SemEval-2014 task4 Restaurant and Laptop dataset?
answer:
BERT-ADA BERT-PT, AEN-BERT, SDGCN-BERT


question:
  In a second step, we will train a neural monolingual translation system, that translates from the output of the PBMT system INLINEFORM0 to a better target sentence INLINEFORM1 . 
 Question: Do they train the NMT model on PBMT outputs?
answer:
Yes


question:
. We could observe that compared with the base model, we have an improvement of 7.36% on accuracy and 9.69% on F1 score.  
 Question: How big are improvements of small-scale unbalanced datasets when sentence representation is enhanced with topic information?
answer:

7.36% on accuracy and 9.69% on F1 score