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.

Example Input: 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?
Example Output: BERT-ADA BERT-PT, AEN-BERT, SDGCN-BERT

Example Input: In this section, we introduce an invertibility condition for our neural projector to tackle the optimization challenge. Specifically, we constrain our neural projector with two requirements: (1) INLINEFORM0 and (2) INLINEFORM1 exists.  
 Question: What is the invertibility condition?
Example Output: The neural projector must be invertible.

Example Input: Similar trends are seen in the performance of other two state-of-the-art approaches BIBREF9 , BIBREF8 . 
 Question: What are the state of the art models?
Example Output:
BIBREF9  BIBREF8