Definition: 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.

Positive Example 1 -
Input: We evaluate the proposed approach on the Chinese social media text summarization task, based on the sequence-to-sequence model. Large-Scale Chinese Short Text Summarization Dataset (LCSTS) is constructed by BIBREF1 . The dataset consists of more than 2.4 million text-summary pairs in total, constructed from a famous Chinese social media microblogging service Weibo.  
 Question: Are results reported only for English data?
Output: No

Positive Example 2 -
Input: Since INLINEFORM0 is constant, we only need to minimize INLINEFORM1 , therefore the loss function becomes: DISPLAYFORM0 
 Question: How is the expectation regularization loss defined?
Output: DISPLAYFORM0

Negative Example 1 -
Input: The minimal change operation substantially changed the meaning of the sentence, and yet the embedding of the transformation lies very closely to the original sentence (average similarity of 0.930). 
 Question: Do they do any analysis of of how the modifications changed the starting set of sentences?
Output: No

Negative Example 2 -
Input: Looking at the current WinoGrande leaderboard, it appears that the previous state of the art is based on RoBERTa BIBREF2, which can be characterized as an encoder-only transformer architecture. 
 Question: What is the previous state of the art?
Output: GPT-3

Now complete the following example -
Input: Construction of the auxiliary sentence
For simplicity, we mainly describe our method with TABSA as an example.

We consider the following four methods to convert the TABSA task into a sentence pair classification task:

The sentence we want to generate from the target-aspect pair is a question, and the format needs to be the same. For example, for the set of a target-aspect pair (LOCATION1, safety), the sentence we generate is “what do you think of the safety of location - 1 ?"

For the NLI task, the conditions we set when generating sentences are less strict, and the form is much simpler. The sentence created at this time is not a standard sentence, but a simple pseudo-sentence, with (LOCATION1, safety) pair as an example: the auxiliary sentence is: “location - 1 - safety".

For QA-B, we add the label information and temporarily convert TABSA into a binary classification problem ( INLINEFORM0 ) to obtain the probability distribution. At this time, each target-aspect pair will generate three sequences such as “the polarity of the aspect safety of location - 1 is positive", “the polarity of the aspect safety of location - 1 is negative", “the polarity of the aspect safety of location - 1 is none". We use the probability value of INLINEFORM1 as the matching score. For a target-aspect pair which generates three sequences ( INLINEFORM2 ), we take the class of the sequence with the highest matching score for the predicted category.

The difference between NLI-B and QA-B is that the auxiliary sentence changes from a question to a pseudo-sentence. The auxiliary sentences are: “location - 1 - safety - positive", “location - 1 - safety - negative", and “location - 1 - safety - none".

After we construct the auxiliary sentence, we can transform the TABSA task from a single sentence classification task to a sentence pair classification task. As shown in Table TABREF19 , this is a necessary operation that can significantly improve the experimental results of the TABSA task. 
 Question: How do they generate the auxiliary sentence?
Output: 



Answer: The sentence we want to generate from the target-aspect pair is a question, and the format needs to be the same. For the NLI task, the conditions we set when generating sentences are less strict, and the form is much simpler. For QA-B, we add the label information and temporarily convert TABSA into a binary classification problem ( INLINEFORM0 ) to obtain the probability distribution auxiliary sentence changes from a question to a pseudo-sentence