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: We calculated inter-annotator agreement by Krippendorff's Alpha BIBREF23, which accounts for different annotator pairs and empty values. To also zoom in on the particular agreement by category, we calculated mutual F-scores for each of the categories. This metric is typically used to evaluate system performance by category on gold standard data, but could also be applied to annotation pairs by alternating the roles of the two annotators between classifier and ground truth. While both the Relevance and Subject categorizations are annotated at a percent agreement of $0.71$ and $0.70$, their agreement scores are only fair, at $\alpha =0.27$ and $\alpha =0.29$. The percent agreement on Stance and Sentiment, which carry more categories than the former two, is $0.54$ for both. Their agreement scores are also fair, at $\alpha =0.35$ and $\alpha =0.34$. The mutual F-scores show marked differences in agreement by category, where the categories that were annotated most often typically yield a higher score. This holds for the Relevant category ($0.81$), the Vaccine category ($0.79$) and the Positive category ($0.64$). The Negative category yields a mutual F-score of $0.42$, which is higher than the more frequently annotated categories Neutral ($0.23$) and Not clear ($0.31$). We found that these categories are often confused. After combining the annotations of the two, the stance agreement would be increased to $\alpha =0.43$. 
 Question: What is the agreement score of their annotated dataset?
Output: 



Answer:  Relevance and Subject categorizations are annotated at a percent agreement of $0.71$ and $0.70$, their agreement scores are only fair, at $\alpha =0.27$ and $\alpha =0.29$ Stance and Sentiment, which carry more categories than the former two, is $0.54$ for both. Their agreement scores are also fair, at $\alpha =0.35$ and $\alpha =0.34$ This holds for the Relevant category ($0.81$), the Vaccine category ($0.79$) and the Positive category ($0.64$)  The Negative category yields a mutual F-score of $0.42$, which is higher than the more frequently annotated categories Neutral ($0.23$) and Not clear ($0.31$).