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: According to the above analysis According to the above analysis, we proposed a weighted version of DIRL to address the problem caused by the shift of $\rm {P}(\rm {Y})$ to DIRL. The key idea of this framework is to first align $\rm {P}(\rm {Y})$ across domains before performing domain-invariant learning, and then take account the shift of $\rm {P}(\rm {Y})$ in the label prediction procedure. Specifically, it introduces a class weight $\mathbf {w}$ to weigh source domain examples by class. Based on the weighted source domain, the domain shift problem is resolved in two steps.  The motivation behind this practice is to adjust data distribution of the source domain or the target domain to alleviate the shift of $\rm {P}(\rm {Y})$ across domains before applying DIRL. Consider that we only have labels of source domain data, we choose to adjust data distribution of the source domain. To achieve this purpose, we introduce a trainable class weight $\mathbf {w}$ to reweigh source domain examples by class when performing DIRL, with $\mathbf {w}_i > 0$. Specifically, we hope that:

and we denote $\mathbf {w}^*$ the value of $\mathbf {w}$ that makes this equation hold.  We shall see that when $\mathbf {w}=\mathbf {w}^*$, DIRL is to align $\rm {P}_S(G(\rm {X})|\rm {Y})$ with $\rm {P}_T(G(\rm {X})|\rm {Y})$ without the shift of $\rm {P}(\rm {Y})$. According to our analysis, we know that due to the shift of $\rm {P}(\rm {Y})$, there is a conflict between the training objects of the supervised learning $\mathcal {L}_{sup}$ and the domain-invariant learning $\mathcal {L}_{inv}$. And the conflict degree will decrease as $\rm {P}_S(\rm {Y})$ getting close to $\rm {P}_T(\rm {Y})$. Therefore, during model training, $\mathbf {w}$ is expected to be optimized toward $\mathbf {w}^*$ since it will make $\rm {P}(\rm {Y})$ of the weighted source domain close to $\rm {P}_T(\rm {Y})$, so as to solve the conflict. 
 Question: How are different domains weighted in WDIRL?
Output: 



Answer: To achieve this purpose, we introduce a trainable class weight $\mathbf {w}$ to reweigh source domain examples by class when performing DIRL, with $\mathbf {w}_i > 0$