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: As shown in Table TABREF39 , the proposed framework consistently obtains the best scores on all of the four datasets. Compared with the winning systems of SemEval ABSA, our framework achieves 5.0%, 1.6%, 1.4%, 1.3% absolute gains on INLINEFORM0 , INLINEFORM1 , INLINEFORM2 and INLINEFORM3 respectively.

Our framework can outperform RNCRF, a state-of-the-art model based on dependency parsing, on all datasets. We also notice that RNCRF does not perform well on INLINEFORM0 and INLINEFORM1 (3.7% and 3.9% inferior than ours). We find that INLINEFORM2 and INLINEFORM3 contain many informal reviews, thus RNCRF's performance degradation is probably due to the errors from the dependency parser when processing such informal texts. 
 Question: By how much do they outperform state-of-the-art methods?
Example Output: Compared with the winning systems of SemEval ABSA, our framework achieves 5.0%, 1.6%, 1.4%, 1.3% absolute gains on INLINEFORM0 , INLINEFORM1 , INLINEFORM2 and INLINEFORM3 respectively.

Example Input: In this work, we use pretraining on the unlabeled data of each task and show that it can increase the performance of classification systems. 
 Question: Where is MVCNN pertained?
Example Output: on the unlabeled data of each task

Example Input: We compare our sentence classification with the LSTM baseline and evaluate the biggest set of PICO sentence data available at this point BIBREF13. SCIBERT was chosen as an additional baseline model for fine-tuning because it provided the best representation of embedded PICO sentences.  
 Question: What baselines did they consider?
Example Output:
LSTM SCIBERT